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electrocardiogram</title> <meta name="description" content="Search results for: electrocardiogram"> <meta name="keywords" content="electrocardiogram"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="electrocardiogram"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 80</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: electrocardiogram</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">80</span> Electrocardiogram Signal Denoising Using a Hybrid Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Latif">R. Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Jenkal"> W. Jenkal</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Toumanari"> A. Toumanari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hatim"> A. Hatim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20technique" title="hybrid technique">hybrid technique</a>, <a href="https://publications.waset.org/abstracts/search?q=ADTF" title=" ADTF"> ADTF</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a> </p> <a href="https://publications.waset.org/abstracts/65458/electrocardiogram-signal-denoising-using-a-hybrid-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">79</span> Recent Advancement in Fetal Electrocardiogram Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Savita">Savita</a>, <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Sharma"> Anurag Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsukhpreet%20Singh"> Harsukhpreet Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fetal Electrocardiogram (fECG) is a widely used technique to assess the fetal well-being and identify any changes that might be with problems during pregnancy and to evaluate the health and conditions of the fetus. Various techniques or methods have been employed to diagnose the fECG from abdominal signal. This paper describes the facile approach for the estimation of the fECG known as Adaptive Comb. Filter (ACF). The ACF can adjust according to the temporal variations in fundamental frequency by itself that used for the estimation of the quasi periodic signal of ECG signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aECG" title="aECG">aECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ACF" title=" ACF"> ACF</a>, <a href="https://publications.waset.org/abstracts/search?q=fECG" title=" fECG"> fECG</a>, <a href="https://publications.waset.org/abstracts/search?q=mECG" title=" mECG"> mECG</a> </p> <a href="https://publications.waset.org/abstracts/49031/recent-advancement-in-fetal-electrocardiogram-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">78</span> Secured Embedding of Patient’s Confidential Data in Electrocardiogram Using Chaotic Maps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Butta%20Singh">Butta Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20maps" title="chaotic maps">chaotic maps</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20steganography" title=" ECG steganography"> ECG steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20embedding" title=" data embedding"> data embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a> </p> <a href="https://publications.waset.org/abstracts/78602/secured-embedding-of-patients-confidential-data-in-electrocardiogram-using-chaotic-maps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">77</span> FlexPoints: Efficient Algorithm for Detection of Electrocardiogram Characteristic Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Bulanda">Daniel Bulanda</a>, <a href="https://publications.waset.org/abstracts/search?q=Janusz%20A.%20Starzyk"> Janusz A. Starzyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Horzyk"> Adrian Horzyk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=characteristic%20points" title="characteristic points">characteristic points</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20compression" title=" signal compression"> signal compression</a> </p> <a href="https://publications.waset.org/abstracts/132090/flexpoints-efficient-algorithm-for-detection-of-electrocardiogram-characteristic-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">76</span> Wavelet-Based Classification of Myocardial Ischemia, Arrhythmia, Congestive Heart Failure and Sleep Apnea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santanu%20Chattopadhyay">Santanu Chattopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Gautam%20Sarkar"> Gautam Sarkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabinda%20Das"> Arabinda Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmia" title="arrhythmia">arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=congestive%20heart%20failure" title=" congestive heart failure"> congestive heart failure</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20ischemia" title=" myocardial ischemia"> myocardial ischemia</a>, <a href="https://publications.waset.org/abstracts/search?q=sleep%20apnea" title=" sleep apnea"> sleep apnea</a> </p> <a href="https://publications.waset.org/abstracts/112333/wavelet-based-classification-of-myocardial-ischemia-arrhythmia-congestive-heart-failure-and-sleep-apnea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112333.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">75</span> Optimal ECG Sampling Frequency for Multiscale Entropy-Based HRV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manjit%20Singh">Manjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiscale entropy (MSE) is an extensively used index to provide a general understanding of multiple complexity of physiologic mechanism of heart rate variability (HRV) that operates on a wide range of time scales. Accurate selection of electrocardiogram (ECG) sampling frequency is an essential concern for clinically significant HRV quantification; high ECG sampling rate increase memory requirements and processing time, whereas low sampling rate degrade signal quality and results in clinically misinterpreted HRV. In this work, the impact of ECG sampling frequency on MSE based HRV have been quantified. MSE measures are found to be sensitive to ECG sampling frequency and effect of sampling frequency will be a function of time scale. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20%28electrocardiogram%29" title="ECG (electrocardiogram)">ECG (electrocardiogram)</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20variability%20%28HRV%29" title=" heart rate variability (HRV)"> heart rate variability (HRV)</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale%20entropy" title=" multiscale entropy"> multiscale entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20frequency" title=" sampling frequency"> sampling frequency</a> </p> <a href="https://publications.waset.org/abstracts/78603/optimal-ecg-sampling-frequency-for-multiscale-entropy-based-hrv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78603.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">271</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">74</span> Electrocardiogram-Based Heartbeat Classification Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jacqueline%20Rose%20T.%20Alipo-on">Jacqueline Rose T. Alipo-on</a>, <a href="https://publications.waset.org/abstracts/search?q=Francesca%20Isabelle%20F.%20Escobar"> Francesca Isabelle F. Escobar</a>, <a href="https://publications.waset.org/abstracts/search?q=Myles%20Joshua%20T.%20Tan"> Myles Joshua T. Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hezerul%20Abdul%20Karim"> Hezerul Abdul Karim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nouar%20Al%20Dahoul"> Nouar Al Dahoul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heartbeat%20classification" title="heartbeat classification">heartbeat classification</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20signals" title=" electrocardiogram signals"> electrocardiogram signals</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet-50" title=" ResNet-50"> ResNet-50</a> </p> <a href="https://publications.waset.org/abstracts/162763/electrocardiogram-based-heartbeat-classification-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162763.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">73</span> Electrical Cardiac Remodeling in Elite Athletes: A Comparative Study between Triathletes and Cyclists</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lingxia%20Li">Lingxia Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Fr%C3%A9d%C3%A9ric%20Schnell"> Frédéric Schnell</a>, <a href="https://publications.waset.org/abstracts/search?q=Thibault%20Lachard"> Thibault Lachard</a>, <a href="https://publications.waset.org/abstracts/search?q=Anne-Charlotte%20Dupont"> Anne-Charlotte Dupont</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuzhe%20Ding"> Shuzhe Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Sol%C3%A8ne%20Le%20Douairon%20Lahaye"> Solène Le Douairon Lahaye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Repetitive participation in triathlon training results in significant myocardial changes. However, whether the cardiac remodeling in triathletes is related to the specificities of the sport (consisting of three sports) raises questions. Methods: Elite triathletes and cyclists registered on the French ministerial lists of high-level athletes were involved. The basic information and routine electrocardiogram records were obtained. Electrocardiograms were evaluated according to clinical criteria. Results: Of the 105 athletes included in the study, 42 were from the short-distance triathlon (40%), and 63 were from the road cycling (60%). The average age was 22.1±4.2 years. The P wave amplitude was significantly lower in triathletes than in cyclists (p=0.005), and no significant statistical difference was found in heart rate, RR interval, PR or PQ interval, QRS complex, QRS axe, QT interval, and QTc (p>0.05). All the measured parameters were within normal ranges. The most common electrical manifestations were early repolarization (60.95%) and incomplete right bundle branch block (43.81%); there was no statistical difference between the groups (p>0.05). Conclusions: Prolonged intensive endurance exercise training induces physiological cardiac remodeling in both triathletes and cyclists. The most common electrocardiogram manifestations were early repolarization and incomplete right bundle branch block. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiac%20screening" title="cardiac screening">cardiac screening</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=triathlon" title=" triathlon"> triathlon</a>, <a href="https://publications.waset.org/abstracts/search?q=cycling" title=" cycling"> cycling</a>, <a href="https://publications.waset.org/abstracts/search?q=elite%20athletes" title=" elite athletes"> elite athletes</a> </p> <a href="https://publications.waset.org/abstracts/194909/electrical-cardiac-remodeling-in-elite-athletes-a-comparative-study-between-triathletes-and-cyclists" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194909.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">72</span> Auto Classification of Multiple ECG Arrhythmic Detection via Machine Learning Techniques: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ng%20Liang%20Shen">Ng Liang Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Hau%20Yuan%20Wen"> Hau Yuan Wen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=QRS" title=" QRS"> QRS</a> </p> <a href="https://publications.waset.org/abstracts/58871/auto-classification-of-multiple-ecg-arrhythmic-detection-via-machine-learning-techniques-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58871.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">71</span> High Performance Electrocardiogram Steganography Based on Fast Discrete Cosine Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liang-Ta%20Cheng">Liang-Ta Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Yu%20Yang"> Ching-Yu Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on fast discrete cosine transform (FDCT), the authors present a high capacity and high perceived quality method for electrocardiogram (ECG) signal. By using a simple adjusting policy to the 1-dimentional (1-D) DCT coefficients, a large volume of secret message can be effectively embedded in an ECG host signal and be successfully extracted at the intended receiver. Simulations confirmed that the resulting perceived quality is good, while the hiding capability of the proposed method significantly outperforms that of existing techniques. In addition, our proposed method has a certain degree of robustness. Since the computational complexity is low, it is feasible for our method being employed in real-time applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20hiding" title="data hiding">data hiding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20steganography" title=" ECG steganography"> ECG steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20discrete%20cosine%20transform" title=" fast discrete cosine transform"> fast discrete cosine transform</a>, <a href="https://publications.waset.org/abstracts/search?q=1-D%20DCT%20bundle" title=" 1-D DCT bundle"> 1-D DCT bundle</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20applications" title=" real-time applications"> real-time applications</a> </p> <a href="https://publications.waset.org/abstracts/81916/high-performance-electrocardiogram-steganography-based-on-fast-discrete-cosine-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">70</span> Compact Low-Voltage Biomedical Instrumentation Amplifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phanumas%20Khumsat">Phanumas Khumsat</a>, <a href="https://publications.waset.org/abstracts/search?q=Chalermchai%20Janmane"> Chalermchai Janmane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low-voltage instrumentation amplifier has been proposed for 3-lead electrocardiogram measurement system. The circuit’s interference rejection technique is based upon common-mode feed-forwarding where common-mode currents have cancelled each other at the output nodes. The common-mode current for cancellation is generated by means of common-mode sensing and emitter or source followers with resistors employing only one transistor. Simultaneously this particular transistor also provides common-mode feedback to the patient’s right/left leg to further reduce interference entering the amplifier. The proposed designs have been verified with simulations in 0.18-µm CMOS process operating under 1.0-V supply with CMRR greater than 80dB. Moreover ECG signals have experimentally recorded with the proposed instrumentation amplifiers implemented from discrete BJT (BC547, BC558) and MOSFET (ALD1106, ALD1107) transistors working with 1.5-V supply. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=common-mode%20feedback" title=" common-mode feedback"> common-mode feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=common-mode%20feedforward" title=" common-mode feedforward"> common-mode feedforward</a>, <a href="https://publications.waset.org/abstracts/search?q=communication%20engineering" title=" communication engineering"> communication engineering</a> </p> <a href="https://publications.waset.org/abstracts/4913/compact-low-voltage-biomedical-instrumentation-amplifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4913.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">69</span> Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tsu-Wang%20Shen">Tsu-Wang Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shan-Chun%20Chang"> Shan-Chun Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Hsien%20Wang"> Chih-Hsien Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Te-Chao%20Fang"> Te-Chao Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-intensity%20heart%20rate" title="high-intensity heart rate">high-intensity heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20resistant" title=" heart rate resistant"> heart rate resistant</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20human%20identification" title=" ECG human identification"> ECG human identification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20based%20artificial%20neural%20network" title=" decision based artificial neural network"> decision based artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/53603/heart-rate-resistance-electrocardiogram-identification-based-on-slope-oriented-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53603.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">434</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">68</span> Detection of Cardiac Arrhythmia Using Principal Component Analysis and Xgboost Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujay%20Kotwale">Sujay Kotwale</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramasubba%20Reddy%20M."> Ramasubba Reddy M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiac%20arrhythmia" title="cardiac arrhythmia">cardiac arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title=" XGBoost"> XGBoost</a> </p> <a href="https://publications.waset.org/abstracts/126916/detection-of-cardiac-arrhythmia-using-principal-component-analysis-and-xgboost-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">67</span> An Auxiliary Technique for Coronary Heart Disease Prediction by Analyzing Electrocardiogram Based on ResNet and Bi-Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Zhang">Yang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20He"> Jian He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title="Bi-LSTM">Bi-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CHD" title=" CHD"> CHD</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%C2%A0window" title=" sliding window"> sliding window</a> </p> <a href="https://publications.waset.org/abstracts/165165/an-auxiliary-technique-for-coronary-heart-disease-prediction-by-analyzing-electrocardiogram-based-on-resnet-and-bi-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165165.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">66</span> Assessment of Arterial Stiffness through Measurement of Magnetic Flux Disturbance and Electrocardiogram Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Niu">Jing Niu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20X.%20Wang"> Jun X. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arterial stiffness predicts mortality and morbidity, independently of other cardiovascular risk factors. And it is a major risk factor for age-related morbidity and mortality. The non-invasive industry gold standard measurement system of arterial stiffness utilizes pulse wave velocity method. However, the desktop device is expensive and requires trained professional to operate. The main objective of this research is the proof of concept of the proposed non-invasive method which uses measurement of magnetic flux disturbance and electrocardiogram (ECG) signal for measuring arterial stiffness. The method could enable accurate and easy self-assessment of arterial stiffness at home, and to help doctors in research, diagnostic and prescription in hospitals and clinics. A platform for assessing arterial stiffness through acquisition and analysis of radial artery pulse waveform and ECG signal has been developed based on the proposed method. Radial artery pulse waveform is acquired using the magnetic based sensing technology, while ECG signal is acquired using two dry contact single arm ECG electrodes. The measurement only requires the participant to wear a wrist strap and an arm band. Participants were recruited for data collection using both the developed platform and the industry gold standard system. The results from both systems underwent correlation assessment analysis. A strong positive correlation between the results of the two systems is observed. This study presents the possibility of developing an accurate, easy to use and affordable measurement device for arterial stiffness assessment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arterial%20stiffness" title="arterial stiffness">arterial stiffness</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20wave%20velocity" title=" pulse wave velocity"> pulse wave velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=Magnetic%20Flux%20Disturbance" title=" Magnetic Flux Disturbance "> Magnetic Flux Disturbance </a> </p> <a href="https://publications.waset.org/abstracts/77673/assessment-of-arterial-stiffness-through-measurement-of-magnetic-flux-disturbance-and-electrocardiogram-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77673.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">65</span> Effects of Acute Exposure to WIFI Signals (2,45 GHz) on Heart Variability and Blood Pressure in Albinos Rabbit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linda%20Saili">Linda Saili</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Hanini"> Amel Hanini</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiraz%20Smirani"> Chiraz Smirani</a>, <a href="https://publications.waset.org/abstracts/search?q=Iness%20Azzouz"> Iness Azzouz</a>, <a href="https://publications.waset.org/abstracts/search?q=Amina%20Azzouz"> Amina Azzouz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafedh%20Abdemelek"> Hafedh Abdemelek</a>, <a href="https://publications.waset.org/abstracts/search?q=Zihad%20Bouslama"> Zihad Bouslama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram and arterial pressure measurements were studied under acute exposures to WIFI (2.45 GHz) during one hour in adult male rabbits. Antennas of WIFI were placed at 25 cm at the right side near the heart. Acute exposure of rabbits to WIFI increased heart frequency (+ 22%) and arterial blood pressure (+14%). Moreover, analysis of ECG revealed that WIFI induced a combined increase of PR and QT intervals. By contrast, the same exposure failed to alter the maximum amplitude and P waves. After intravenously injection of dopamine (0.50 ml/kg) and epinephrine (0.50ml/kg) under acute exposure to RF we found that WIFI alter catecholamines(dopamine, epinephrine) action on heart variability and blood pressure compared to control. These results suggest for the first time, as far as we know, that exposure to WIFI affect heart rhythm, blood pressure, and catecholamines efficacy on cardiovascular system; indicating that radio frequency can act directly and/or indirectly on the cardiovascular system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20%28HR%29" title="heart rate (HR)">heart rate (HR)</a>, <a href="https://publications.waset.org/abstracts/search?q=arterial%20pressure%20%28PA%29" title=" arterial pressure (PA)"> arterial pressure (PA)</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20%28ECG%29" title=" electrocardiogram (ECG)"> electrocardiogram (ECG)</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20efficacy%20of%0D%0Acatecholamines" title=" the efficacy of catecholamines"> the efficacy of catecholamines</a>, <a href="https://publications.waset.org/abstracts/search?q=dopamine" title=" dopamine"> dopamine</a>, <a href="https://publications.waset.org/abstracts/search?q=epinephrine" title=" epinephrine"> epinephrine</a> </p> <a href="https://publications.waset.org/abstracts/40803/effects-of-acute-exposure-to-wifi-signals-245-ghz-on-heart-variability-and-blood-pressure-in-albinos-rabbit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40803.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">64</span> A Case Series on Isolated Lead aVR ST-Segment Elevation Clinical Significance and Outcome</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fae%20Princess%20Bermudez">Fae Princess Bermudez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: One of the least significant leads on a 12-lead electrocardiogram is the augmented right lead (aVR), as it is not as specific compared to the other leads. In this case series, the value of lead aVR, which is more often than not ignored, is highlighted. Three cases of aVR ST segment elevation on 12-lead electrocardiogram are described, with the end outcome of demise of all three patients. The importance of immediate revascularization is described to improve prognosis in this group of patients. Objectives: This case series aims to primarily present under-reported cases of isolated aVR ST-segrment elevation myocardial infarction (STEMI), their course and outcome. More specific aims are to identify the criteria in determination of isolated aVR STEMI, know its clinical significance, and determine appropriate management for patients with this ECG finding. Method: A short review of previous studies, case reports, articles and guidelines from 2011-2016 was done. The author reviewed available literature, sorted out those that proved to be significant for the presented cases, and described them in conjunction with the aforementioned cases. Findings: Based on the limited information on these rare or under-reported cases, it was found that isolated aVR STEMI had a poorer prognosis that led to significant mortality and morbidity of patients. The significance of aVR ST-elevation was that of an occlusion of the left coronary artery or a severe three-vessel disease in the presence of an Acute Coronary Syndrome. Guidelines from American Heart Association/American College of Cardiology Foundation in 2013 already recognized ST-elevation of lead aVR in isolation as a STEMI; hence, recommended that patients with this particular ECG finding should undergo reperfusion strategies to improve prognosis. Conclusion: The indispensability of isolated aVR ST-segment elevation on ECG should alert physicians, especially Emergency physicians, to the high probability of Acute Coronary Syndrome with a very poor prognosis. If this group of patients is not promptly managed, demise may ensue, with cardiogenic shock as the most probable cause. With this electrocardiogram finding, physicians must be quick to make clinical decisions to increase chances of survival of this group of patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AVR%20ST-elevation" title="AVR ST-elevation">AVR ST-elevation</a>, <a href="https://publications.waset.org/abstracts/search?q=diffuse%20ST-segment%20depression" title=" diffuse ST-segment depression"> diffuse ST-segment depression</a>, <a href="https://publications.waset.org/abstracts/search?q=left%20coronary%20artery%20infarction" title=" left coronary artery infarction"> left coronary artery infarction</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20infarction" title=" myocardial infarction"> myocardial infarction</a> </p> <a href="https://publications.waset.org/abstracts/69856/a-case-series-on-isolated-lead-avr-st-segment-elevation-clinical-significance-and-outcome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">209</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">63</span> Low Power CMOS Amplifier Design for Wearable Electrocardiogram Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ow%20Tze%20Weng">Ow Tze Weng</a>, <a href="https://publications.waset.org/abstracts/search?q=Suhaila%20Isaak"> Suhaila Isaak</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusmeeraz%20Yusof"> Yusmeeraz Yusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability, especially in the most common electrocardiogram (ECG) monitoring system. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip (SoC) is increasing in exponential way, the front end ECG sensors are still suffering from flicker noise for low frequency cardiac signal acquisition, 50 Hz power line electromagnetic interference, and the large unstable input offsets due to the electrode-skin interface is not attached properly. In this paper, a high performance CMOS amplifier for ECG sensors that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using 0.13 µm CMOS technology from Silterra, the simulation results show that this front end circuit can achieve a very low input referred noise of 1 pV/√Hz and high common mode rejection ratio (CMRR) of 174.05 dB. It also gives voltage gain of 75.45 dB with good power supply rejection ratio (PSSR) of 92.12 dB. The total power consumption is only 3 µW and thus suitable to be implemented with further signal processing and classification back end for low power biomedical SoC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CMOS" title="CMOS">CMOS</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=amplifier" title=" amplifier"> amplifier</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20power" title=" low power"> low power</a> </p> <a href="https://publications.waset.org/abstracts/78090/low-power-cmos-amplifier-design-for-wearable-electrocardiogram-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">248</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">62</span> Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Jianhong">Xiang Jianhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Cong"> Wang Cong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Linyu"> Wang Linyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title="telemedicine">telemedicine</a>, <a href="https://publications.waset.org/abstracts/search?q=fetal%20ECG" title=" fetal ECG"> fetal ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20sparse%20reconstruction" title=" joint sparse reconstruction"> joint sparse reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20sparse%20signal" title=" block sparse signal"> block sparse signal</a> </p> <a href="https://publications.waset.org/abstracts/154614/compressed-sensing-of-fetal-electrocardiogram-signals-based-on-joint-block-multi-orthogonal-least-squares-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">61</span> Effects of Lower and Upper Body Plyometric Training on Electrocardiogram Parameters of University Athletes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20N.%20Uzor">T. N. Uzor</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20O.%20Akosile"> C. O. Akosile</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20O.%20Emeahara"> G. O. Emeahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plyometric training is a form of specialised strength training that uses fast muscular contractions to improve power and speed in sports conditioning by coaches and athletes. Despite its useful role in sports conditioning programme, the information about plyometric training on the athletes cardiovascular health especially Electrocardiogram (ECG) has not been established in the literature. The purpose of the study was to determine the effects of lower and upper body plyometric training on ECG of athletes. The study was guided by three null hypotheses. Quasi–experimental research design was adopted for the study. Seventy-two university male athletes constituted the population of the study. Thirty male athletes aged 18 to 24 years volunteered to participate in the study, but only twenty-three completed the study. The volunteered athletes were apparently healthy, physically active and free of any lower and upper extremity bone injuries for past one year and they had no medical or orthopedic injuries that may affect their participation in the study. Ten subjects were purposively assigned to one of the three groups: lower body plyometric training (LBPT), upper body plyometric training (UBPT), and control (C). Training consisted of six plyometric exercises: lower (ankle hops, squat jumps, tuck jumps) and upper body plyometric training (push-ups, medicine ball-chest throws and side throws) with moderate intensity. The general data were collated and analysed using Statistical Package for Social Science (SPSS version 22.0). The research questions were answered using mean and standard deviation, while paired samples t-test was also used to test for the hypotheses. The results revealed that athletes who were trained using LBPT had reduced ECG parameters better than those in the control group. The results also revealed that athletes who were trained using both LBPT and UBPT indicated lack of significant differences following ten weeks plyometric training than those in the control group in the ECG parameters except in Q wave, R wave and S wave (QRS) complex. Based on the findings of the study, it was recommended among others that coaches should include both LBPT and UBPT as part of athletes’ overall training programme from primary to tertiary institution to optimise performance as well as reduce the risk of cardiovascular diseases and promotes good healthy lifestyle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concentric" title="concentric">concentric</a>, <a href="https://publications.waset.org/abstracts/search?q=eccentric" title=" eccentric"> eccentric</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=plyometric" title=" plyometric"> plyometric</a> </p> <a href="https://publications.waset.org/abstracts/95026/effects-of-lower-and-upper-body-plyometric-training-on-electrocardiogram-parameters-of-university-athletes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95026.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">60</span> Denoising Convolutional Neural Network Assisted Electrocardiogram Signal Watermarking for Secure Transmission in E-Healthcare Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Rani">Jyoti Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashima%20Anand"> Ashima Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivendra%20Shivani"> Shivendra Shivani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, physiological signals obtained in telemedicine have been stored independently from patient information. In addition, people have increasingly turned to mobile devices for information on health-related topics. Major authentication and security issues may arise from this storing, degrading the reliability of diagnostics. This study introduces an approach to reversible watermarking, which ensures security by utilizing the electrocardiogram (ECG) signal as a carrier for embedding patient information. In the proposed work, Pan-Tompkins++ is employed to convert the 1D ECG signal into a 2D signal. The frequency subbands of a signal are extracted using RDWT(Redundant discrete wavelet transform), and then one of the subbands is subjected to MSVD (Multiresolution singular valued decomposition for masking. Finally, the encrypted watermark is embedded within the signal. The experimental results show that the watermarked signal obtained is indistinguishable from the original signals, ensuring the preservation of all diagnostic information. In addition, the DnCNN (Denoising convolutional neural network) concept is used to denoise the retrieved watermark for improved accuracy. The proposed ECG signal-based watermarking method is supported by experimental results and evaluations of its effectiveness. The results of the robustness tests demonstrate that the watermark is susceptible to the most prevalent watermarking attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG" title="ECG">ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=VMD" title=" VMD"> VMD</a>, <a href="https://publications.waset.org/abstracts/search?q=watermarking" title=" watermarking"> watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=PanTompkins%2B%2B" title=" PanTompkins++"> PanTompkins++</a>, <a href="https://publications.waset.org/abstracts/search?q=RDWT" title=" RDWT"> RDWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DnCNN" title=" DnCNN"> DnCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=MSVD" title=" MSVD"> MSVD</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20encryption" title=" chaotic encryption"> chaotic encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=attacks" title=" attacks"> attacks</a> </p> <a href="https://publications.waset.org/abstracts/174732/denoising-convolutional-neural-network-assisted-electrocardiogram-signal-watermarking-for-secure-transmission-in-e-healthcare-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174732.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> Portable Cardiac Monitoring System Based on Real-Time Microcontroller and Multiple Communication Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ionel%20Zagan">Ionel Zagan</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasile%20Gheorghita%20Gaitan"> Vasile Gheorghita Gaitan</a>, <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Brezulianu"> Adrian Brezulianu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the contributions in designing a mobile system named Tele-ECG implemented for remote monitoring of cardiac patients. For a better flexibility of this application, the authors chose to implement a local memory and multiple communication interfaces. The project described in this presentation is based on the ARM Cortex M0+ microcontroller and the ADAS1000 dedicated chip necessary for the collection and transmission of Electrocardiogram signals (ECG) from the patient to the microcontroller, without altering the performances and the stability of the system. The novelty brought by this paper is the implementation of a remote monitoring system for cardiac patients, having a real-time behavior and multiple interfaces. The microcontroller is responsible for processing digital signals corresponding to ECG and also for the implementation of communication interface with the main server, using GSM/Bluetooth SIMCOM SIM800C module. This paper translates all the characteristics of the Tele-ECG project representing a feasible implementation in the biomedical field. Acknowledgment: This paper was supported by the project 'Development and integration of a mobile tele-electrocardiograph in the GreenCARDIO© system for patients monitoring and diagnosis - m-GreenCARDIO', Contract no. BG58/30.09.2016, PNCDI III, Bridge Grant 2016, using the infrastructure from the project 'Integrated Center for research, development and innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for fabrication and control', Contract No. 671/09.04.2015, Sectoral Operational Program for Increase of the Economic Competitiveness co-funded from the European Regional Development Fund. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tele-ECG" title="Tele-ECG">Tele-ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20cardiac%20monitoring" title=" real-time cardiac monitoring"> real-time cardiac monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=microcontroller" title=" microcontroller"> microcontroller</a> </p> <a href="https://publications.waset.org/abstracts/62189/portable-cardiac-monitoring-system-based-on-real-time-microcontroller-and-multiple-communication-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">58</span> Biosignal Recognition for Personal Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadri%20Hussain">Hadri Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=M.Nasir%20Ibrahim"> M.Nasir Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariani%20Idroas"> Mariani Idroas</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuad%20Numan"> Fuad Numan</a>, <a href="https://publications.waset.org/abstracts/search?q=Alias%20Mohd%20Noor"> Alias Mohd Noor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=phonocardiogram" title=" phonocardiogram"> phonocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title=" hidden markov model"> hidden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients" title=" mel frequency cepstral coeffiecients"> mel frequency cepstral coeffiecients</a>, <a href="https://publications.waset.org/abstracts/search?q=client%20identification" title=" client identification"> client identification</a> </p> <a href="https://publications.waset.org/abstracts/48382/biosignal-recognition-for-personal-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">57</span> Lessons Learnt from a Patient with Pseudohyperkalaemia Secondary to Polycythaemia Rubra Vera in a Neuro-ICU Patient Resulting in Dangerous Interventions: Lessons Learnt on Patient Safety Improvement </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dinoo%20Kirthinanda">Dinoo Kirthinanda</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujani%20Wijeratne"> Sujani Wijeratne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pseudohyperkalaemia is a common benign in vitro phenomenon caused by the release of potassium ions (K+) from cells during specimen processing. Analysis of haemolysed blood samples for predominantly intracellular electrolytes may lead to re-investigation and potentially harmful interventions. We report a case of a 52-year male with myeloproliferative disease manifested as Polycythaemia Rubra Vera, Hypertension and hypertensive nephropathy with stage 3 chronic kidney disease admitted to Neuro-intensive care unit (NICU) with an intra-cerebral haemorrhage secondary to hypertensive bleed. His initial blood investigations showed hyperkalemia with serum K+ 6.2 mmol/L yet the bedside arterial blood gas analysis yielded K+ of 4.6 mmol/L. The patient was however given hyperkalemia regime twice based on venous electrolyte analysis. The discrepancy between the bedside electrolyte analysis using arterial blood and venous blood prompted further evaluation. The 12 lead Electrocardiogram showed U waves and sinus bradycardia corresponding to the serum K+ of 2.8 mmol/L on arterial blood gas analysis. Immediate K+ replacement ensured the patient did not develop life-threatening cardiac complications. Pseudohyperkalaemia may pose diagnostic challenges in the absence of detectable haemolysis and should be suspected in susceptible patients with normal Electrocardiogram and Glomerular Filtration Rate to avoid potentially life-threatening interventions. When in doubt, rapid analysis of arterial blood gas may be useful for accurate quantification of potassium. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=patient%20safety" title="patient safety">patient safety</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudohyperkalaemia" title=" pseudohyperkalaemia"> pseudohyperkalaemia</a>, <a href="https://publications.waset.org/abstracts/search?q=haemolysis" title=" haemolysis"> haemolysis</a>, <a href="https://publications.waset.org/abstracts/search?q=myeloproliferative%20disorder" title=" myeloproliferative disorder"> myeloproliferative disorder</a> </p> <a href="https://publications.waset.org/abstracts/105813/lessons-learnt-from-a-patient-with-pseudohyperkalaemia-secondary-to-polycythaemia-rubra-vera-in-a-neuro-icu-patient-resulting-in-dangerous-interventions-lessons-learnt-on-patient-safety-improvement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">56</span> Artificial Intelligence Based Online Monitoring System for Cardiac Patient</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Qasim%20Gilani">Syed Qasim Gilani</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Umair"> Muhammad Umair</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Noman"> Muhammad Noman</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Bilawal%20Shah"> Syed Bilawal Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Aqib%20Abbasi"> Aqib Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Waheed"> Muhammad Waheed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiovascular%20disease" title="cardiovascular disease">cardiovascular disease</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=blood%20pressure" title=" blood pressure"> blood pressure</a> </p> <a href="https://publications.waset.org/abstracts/94753/artificial-intelligence-based-online-monitoring-system-for-cardiac-patient" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94753.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">55</span> Remote Wireless Patient Monitoring System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagar%20R.%20Patil">Sagar R. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinesh%20R.%20Gawade"> Dinesh R. Gawade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhir%20N.%20Divekar"> Sudhir N. Divekar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the medical devices we found when we visit a hospital care unit such device is ‘patient monitoring system’. This device (patient monitoring system) informs doctors and nurses about the patient’s physiological signals. However, this device (patient monitoring system) does not have a remote monitoring capability, which is necessitates constant onsite attendance by support personnel (doctors and nurses). Thus, we have developed a Remote Wireless Patient Monitoring System using some biomedical sensors and Android OS, which is a portable patient monitoring. This device(Remote Wireless Patient Monitoring System) monitors the biomedical signals of patients in real time and sends them to remote stations (doctors and nurse’s android Smartphone and web) for display and with alerts when necessary. Wireless Patient Monitoring System different from conventional device (Patient Monitoring system) in two aspects: First its wireless communication capability allows physiological signals to be monitored remotely and second, it is portable so patients can move while there biomedical signals are being monitor. Wireless Patient Monitoring is also notable because of its implementation. We are integrated four sensors such as pulse oximeter (SPO2), thermometer, respiration, blood pressure (BP), heart rate and electrocardiogram (ECG) in this device (Wireless Patient Monitoring System) and Monitoring and communication applications are implemented on the Android OS using threads, which facilitate the stable and timely manipulation of signals and the appropriate sharing of resources. The biomedical data will be display on android smart phone as well as on web Using web server and database system we can share these physiological signals with remote place medical personnel’s or with any where in the world medical personnel’s. We verified that the multitasking implementation used in the system was suitable for patient monitoring and for other Healthcare applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=patient%20monitoring" title="patient monitoring">patient monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20patient%20monitoring" title=" wireless patient monitoring"> wireless patient monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-medical%20signals" title=" bio-medical signals"> bio-medical signals</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signals" title=" physiological signals"> physiological signals</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20system" title=" embedded system"> embedded system</a>, <a href="https://publications.waset.org/abstracts/search?q=Android%20OS" title=" Android OS"> Android OS</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20oximeter%20%28SPO2%29" title=" pulse oximeter (SPO2)"> pulse oximeter (SPO2)</a>, <a href="https://publications.waset.org/abstracts/search?q=thermometer" title=" thermometer"> thermometer</a>, <a href="https://publications.waset.org/abstracts/search?q=respiration" title=" respiration"> respiration</a>, <a href="https://publications.waset.org/abstracts/search?q=blood%20pressure%20%28BP%29" title=" blood pressure (BP)"> blood pressure (BP)</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate" title=" heart rate"> heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20%28ECG%29" title=" electrocardiogram (ECG)"> electrocardiogram (ECG)</a> </p> <a href="https://publications.waset.org/abstracts/26470/remote-wireless-patient-monitoring-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">54</span> Analysis of a IncResU-Net Model for R-Peak Detection in ECG Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Beatriz%20Lafuente%20Alc%C3%A1zar">Beatriz Lafuente Alcázar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yash%20Wani"> Yash Wani</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20J.%20Nimunkar"> Amit J. Nimunkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmia" title="arrhythmia">arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=R-peaks" title=" R-peaks"> R-peaks</a> </p> <a href="https://publications.waset.org/abstracts/153003/analysis-of-a-incresu-net-model-for-r-peak-detection-in-ecg-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">53</span> A Quality Index Optimization Method for Non-Invasive Fetal ECG Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lucia%20Billeci">Lucia Billeci</a>, <a href="https://publications.waset.org/abstracts/search?q=Gennaro%20Tartarisco"> Gennaro Tartarisco</a>, <a href="https://publications.waset.org/abstracts/search?q=Maurizio%20Varanini"> Maurizio Varanini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fetal%20electrocardiography" title="fetal electrocardiography">fetal electrocardiography</a>, <a href="https://publications.waset.org/abstracts/search?q=fetal%20QRS%20detection" title=" fetal QRS detection"> fetal QRS detection</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis%20%28ICA%29" title=" independent component analysis (ICA)"> independent component analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=wearable" title=" wearable"> wearable</a> </p> <a href="https://publications.waset.org/abstracts/51208/a-quality-index-optimization-method-for-non-invasive-fetal-ecg-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">52</span> Physiological and Psychological Influence on Office Workers during Demand Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Megumi%20Nishida">Megumi Nishida</a>, <a href="https://publications.waset.org/abstracts/search?q=Naoya%20Motegi"> Naoya Motegi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takurou%20Kikuchi"> Takurou Kikuchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoko%20Tokumura"> Tomoko Tokumura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, power system has been changed and flexible power pricing system such as demand response has been sought in Japan. The demand response system is simple in the household sector and the owner, decision-maker, can gain the benefits of power saving. On the other hand, the execution of the demand response in the office building is more complex than household because various people such as owners, building administrators and occupants are involved in making decisions. While the owners benefit from the demand saving, the occupants are forced to be exposed to demand-saved environment certain benefits. One of the reasons is that building systems are usually centralized control and each occupant cannot choose either participate demand response event or not, and contribution of each occupant to demand response is unclear to provide incentives. However, the recent development of IT and building systems enables the personalized control of office environment where each occupant can control the lighting level or temperature around him or herself. Therefore, it can be possible to have a system which each occupant can make a decision of demand response participation in office building. This study investigates the personal behavior upon demand response requests, under the condition where each occupant can adjust their brightness individually in their workspace. Once workers participate in the demand response, their task lights are automatically turned off. The participation rates in the demand response events are compared between four groups which are divided by different motivation, the presence or absence of incentives and the way of participation. The result shows that there are the significant differences of participation rates in demand response event between four groups. The way of participation has a large effect on the participation rate. ‘Opt-out’ group, where the occupants are automatically enrolled in a demand response event if they don't express non-participation, will have the highest participation rate in the four groups. The incentive has also an effect on the participation rate. This study also reports that the impact of low illumination office environment on the occupants, such as stress or fatigue. The electrocardiogram and the questionnaire are used to investigate the autonomic nervous activity and subjective symptoms about the fatigue of the occupants. There is no big difference between dim workspace during demand response event and bright workspace in autonomic nervous activity and fatigue. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demand%20response" title="demand response">demand response</a>, <a href="https://publications.waset.org/abstracts/search?q=illumination" title=" illumination"> illumination</a>, <a href="https://publications.waset.org/abstracts/search?q=questionnaire" title=" questionnaire"> questionnaire</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a> </p> <a href="https://publications.waset.org/abstracts/32950/physiological-and-psychological-influence-on-office-workers-during-demand-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32950.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">351</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">51</span> Selecting the Best RBF Neural Network Using PSO Algorithm for ECG Signal Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Mohsenifar">Najmeh Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Narjes%20Mohsenifar"> Narjes Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Kargar"> Abbas Kargar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20artificial%20neural%20network" title=" RBF artificial neural network"> RBF artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO%20algorithm" title=" PSO algorithm"> PSO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=predict" title=" predict"> predict</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a> </p> <a href="https://publications.waset.org/abstracts/33466/selecting-the-best-rbf-neural-network-using-pso-algorithm-for-ecg-signal-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33466.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">626</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=electrocardiogram&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=electrocardiogram&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=electrocardiogram&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div 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