Search results for: phonocardiogram

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phonocardiogram</title> <meta name="description" content="Search results for: phonocardiogram"> <meta name="keywords" content="phonocardiogram"> <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="phonocardiogram"> <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> 6</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: phonocardiogram</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Robust Heart Sounds Segmentation Based on the Variation of the Phonocardiogram Curve Length</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mecheri%20Zeid%20Belmecheri">Mecheri Zeid Belmecheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Maamar%20Ahfir"> Maamar Ahfir</a>, <a href="https://publications.waset.org/abstracts/search?q=Izzet%20Kale"> Izzet Kale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20sounds" title="heart sounds">heart sounds</a>, <a href="https://publications.waset.org/abstracts/search?q=PCG%20segmentation" title=" PCG segmentation"> PCG segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=event%20detection" title=" event detection"> event detection</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20networks" title=" recurrent neural networks"> recurrent neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=PCG%20curve%20length" title=" PCG curve length"> PCG curve length</a> </p> <a href="https://publications.waset.org/abstracts/157289/robust-heart-sounds-segmentation-based-on-the-variation-of-the-phonocardiogram-curve-length" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157289.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">178</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">5</span> Heart Murmurs and Heart Sounds Extraction Using an Algorithm Process Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Mokeddem">Fatima Mokeddem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phonocardiogram%20signal" title="phonocardiogram signal">phonocardiogram signal</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=Envelope" title=" Envelope"> Envelope</a>, <a href="https://publications.waset.org/abstracts/search?q=Detection" title=" Detection"> Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=murmurs" title=" murmurs"> murmurs</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20sounds" title=" heart sounds"> heart sounds</a> </p> <a href="https://publications.waset.org/abstracts/114970/heart-murmurs-and-heart-sounds-extraction-using-an-algorithm-process-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114970.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">140</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">4</span> Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Masood%20Khan">Nadia Masood Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Salman%20Khan"> Muhammad Salman Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Gul%20Muhammad%20Khan"> Gul Muhammad Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%. <p class="card-text"><strong>Keywords:</strong> <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title="computer aided diagnosis">computer aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20sound%20classification" title=" heart sound classification"> heart sound classification</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20feature%20extraction" title=" and feature extraction"> and feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/95434/automated-heart-sound-classification-from-unsegmented-phonocardiogram-signals-using-time-frequency-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95434.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">263</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">3</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鈥檚 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">2</span> Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia%20Xin%20Low">Jia Xin Low</a>, <a href="https://publications.waset.org/abstracts/search?q=Keng%20Wah%20Choo"> Keng Wah Choo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal. <p class="card-text"><strong>Keywords:</strong> <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=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</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=heart%20sound%20classification" title=" heart sound classification"> heart sound classification</a> </p> <a href="https://publications.waset.org/abstracts/85039/automatic-classification-of-periodic-heart-sounds-using-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85039.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">348</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">1</span> A Physiological Approach for Early Detection of Hemorrhage </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabie%20Fadil">Rabie Fadil</a>, <a href="https://publications.waset.org/abstracts/search?q=Parshuram%20Aarotale"> Parshuram Aarotale</a>, <a href="https://publications.waset.org/abstracts/search?q=Shubha%20Majumder"> Shubha Majumder</a>, <a href="https://publications.waset.org/abstracts/search?q=Bijay%20Guargain"> Bijay Guargain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 卤 6 year, weight:73 卤 11 kg, height:172 卤 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85卤4 bpm vs 68 卤 5bpm, p < 0.01), ST (191 卤 10 ms vs 253 卤 31 ms, p < 0.05), LVET (350 卤 14 ms vs 479 卤 47 ms, p < 0.05) and DT (551 卤 22 ms vs 683 卤 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20pressure" title="blood pressure">blood pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=hemorrhage" title=" hemorrhage"> hemorrhage</a>, <a href="https://publications.waset.org/abstracts/search?q=lower-body%20negative%20pressure" title=" lower-body negative pressure"> lower-body negative pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=LBNP" title=" LBNP"> LBNP</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning "> machine learning </a> </p> <a href="https://publications.waset.org/abstracts/114872/a-physiological-approach-for-early-detection-of-hemorrhage" class="btn btn-primary 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Search results for: phonocardiogram