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seizure</title> <meta name="description" content="Search results for: PTZ-induced seizure"> <meta name="keywords" content="PTZ-induced seizure"> <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="PTZ-induced seizure"> <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> 78</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: PTZ-induced seizure</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">78</span> Epileptic Seizure Prediction Focusing on Relative Change in Consecutive Segments of EEG Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Zavid%20Parvez">Mohammad Zavid Parvez</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoranjan%20Paul"> Manoranjan Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20correlation" title=" phase correlation"> phase correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a> </p> <a href="https://publications.waset.org/abstracts/38611/epileptic-seizure-prediction-focusing-on-relative-change-in-consecutive-segments-of-eeg-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38611.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">308</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> Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natalia%20%20Espinosa">Natalia Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Amorim"> Arthur Amorim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudolf%20%20Huebner"> Rudolf Huebner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANN%29" title="Artificial Neural Network (ANN)">Artificial Neural Network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Epilepsy%20Detection" title=" Epilepsy Detection "> Epilepsy Detection </a>, <a href="https://publications.waset.org/abstracts/search?q=Seizure." title=" Seizure."> Seizure.</a> </p> <a href="https://publications.waset.org/abstracts/122872/feedforward-neural-network-with-backpropagation-for-epilepsy-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122872.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">223</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> Automatic Seizure Detection Using Weighted Permutation Entropy and Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noha%20Seddik">Noha Seddik</a>, <a href="https://publications.waset.org/abstracts/search?q=Sherine%20Youssef"> Sherine Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Kholeif"> Mohamed Kholeif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20detection" title=" epileptic seizure detection"> epileptic seizure detection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20permutation%20entropy%20%28WPE%29" title=" weighted permutation entropy (WPE)"> weighted permutation entropy (WPE)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/12444/automatic-seizure-detection-using-weighted-permutation-entropy-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12444.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">371</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> Preliminary Results on a Maximum Mean Discrepancy Approach for Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boumediene%20Hamzi">Boumediene Hamzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Turky%20N.%20AlOtaiby"> Turky N. AlOtaiby</a>, <a href="https://publications.waset.org/abstracts/search?q=Saleh%20AlShebeili"> Saleh AlShebeili</a>, <a href="https://publications.waset.org/abstracts/search?q=Arwa%20AlAnqary"> Arwa AlAnqary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=kernel%20methods" title="kernel methods">kernel methods</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20mean%20discrepancy" title=" maximum mean discrepancy"> maximum mean discrepancy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20detection" title=" seizure detection"> seizure detection</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/83558/preliminary-results-on-a-maximum-mean-discrepancy-approach-for-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83558.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">238</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> Enhanced Extra Trees Classifier for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maurice%20Ntahobari">Maurice Ntahobari</a>, <a href="https://publications.waset.org/abstracts/search?q=Levin%20Kuhlmann"> Levin Kuhlmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Boley"> Mario Boley</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhinoos%20Razavi%20Hesabi"> Zhinoos Razavi Hesabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection. <p class="card-text"><strong>Keywords:</strong> <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=seizure%20prediction" title=" seizure prediction"> seizure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=extra%20tree%20classifier" title=" extra tree classifier"> extra tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SHAP" title=" SHAP"> SHAP</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a> </p> <a href="https://publications.waset.org/abstracts/155126/enhanced-extra-trees-classifier-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155126.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">112</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> Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Zavid%20Parvez">Mohammad Zavid Parvez</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoranjan%20Paul"> Manoranjan Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Epilepsy" title="Epilepsy">Epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20correlation" title=" phase correlation"> phase correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=fluctuation" title=" fluctuation"> fluctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=deviation." title=" deviation. "> deviation. </a> </p> <a href="https://publications.waset.org/abstracts/37585/epileptic-seizure-prediction-by-exploiting-signal-transitions-phenomena" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37585.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">467</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> Managing Psychogenic Non-Epileptic Seizure Disorder: The Benefits of Collaboration between Psychiatry and Neurology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Donald%20Kushon">Donald Kushon</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Pillai"> Jyoti Pillai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Psychogenic Non-epileptic Seizure Disorder (PNES) is a challenging clinical problem for the neurologist. This study explores the benefits of on-site collaboration between psychiatry and neurology in the management of PNES. A 3 month period at a university hospital seizure clinic is described detailing specific management approaches taken as a result of this collaboration. This study describes four areas of interest: (1. After the video EEG results confirm the diagnosis of PNES, the presentation of the diagnosis of PNES to the patient. (2. The identification of co-morbid psychiatric illness (3. Treatment with specific psychotherapeutic interventions (including Cognitive Behavioral Therapy) and psychopharmacologic interventions (primarily SSRIs) and (4. Preliminary treatment outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20behavioral%20therapy%20%28CBT%29" title="cognitive behavioral therapy (CBT)">cognitive behavioral therapy (CBT)</a>, <a href="https://publications.waset.org/abstracts/search?q=psychogenic%20non-epileptic%20seizure%20disorder%20%28PNES%29" title=" psychogenic non-epileptic seizure disorder (PNES)"> psychogenic non-epileptic seizure disorder (PNES)</a>, <a href="https://publications.waset.org/abstracts/search?q=selective%20serotonin%20reuptake%20inhibitors%20%28SSRIs%29" title=" selective serotonin reuptake inhibitors (SSRIs)"> selective serotonin reuptake inhibitors (SSRIs)</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20electroencephalogram%20%28VEEG%29" title=" video electroencephalogram (VEEG)"> video electroencephalogram (VEEG)</a> </p> <a href="https://publications.waset.org/abstracts/54394/managing-psychogenic-non-epileptic-seizure-disorder-the-benefits-of-collaboration-between-psychiatry-and-neurology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54394.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">315</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> Seizure Effects of FP Bearings on the Seismic Reliability of Base-Isolated Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paolo%20Castaldo">Paolo Castaldo</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruno%20Palazzo"> Bruno Palazzo</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Lodato"> Laura Lodato</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study deals with the seizure effects of friction pendulum (FP) bearings on the seismic reliability of a 3D base-isolated nonlinear structural system, designed according to Italian seismic code (NTC08). The isolated system consists in a 3D reinforced concrete superstructure, a r.c. substructure and the FP devices, described by employing a velocity dependent model. The seismic input uncertainty is considered as a random variable relevant to the problem, by employing a set of natural seismic records selected in compliance with L’Aquila (Italy) seismic hazard as provided from NTC08. Several non-linear dynamic analyses considering the three components of each ground motion have been performed with the aim to evaluate the seismic reliability of the superstructure, substructure, and isolation level, also taking into account the seizure event of the isolation devices. Finally, a design solution aimed at increasing the seismic robustness of the base-isolated systems with FPS is analyzed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FP%20devices" title="FP devices">FP devices</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20reliability" title=" seismic reliability"> seismic reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20robustness" title=" seismic robustness"> seismic robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a> </p> <a href="https://publications.waset.org/abstracts/55083/seizure-effects-of-fp-bearings-on-the-seismic-reliability-of-base-isolated-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55083.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">412</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> Analysis of Epileptic Electroencephalogram Using Detrended Fluctuation and Recurrence Plots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinalini%20Ranjan">Mrinalini Ranjan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudheesh%20Chethil"> Sudheesh Chethil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a common neurological disorder characterised by the recurrence of seizures. Electroencephalogram (EEG) signals are complex biomedical signals which exhibit nonlinear and nonstationary behavior. We use two methods 1) Detrended Fluctuation Analysis (DFA) and 2) Recurrence Plots (RP) to capture this complex behavior of EEG signals. DFA considers fluctuation from local linear trends. Scale invariance of these signals is well captured in the multifractal characterisation using detrended fluctuation analysis (DFA). Analysis of long-range correlations is vital for understanding the dynamics of EEG signals. Correlation properties in the EEG signal are quantified by the calculation of a scaling exponent. We report the existence of two scaling behaviours in the epileptic EEG signals which quantify short and long-range correlations. To illustrate this, we perform DFA on extant ictal (seizure) and interictal (seizure free) datasets of different patients in different channels. We compute the short term and long scaling exponents and report a decrease in short range scaling exponent during seizure as compared to pre-seizure and a subsequent increase during post-seizure period, while the long-term scaling exponent shows an increase during seizure activity. Our calculation of long-term scaling exponent yields a value between 0.5 and 1, thus pointing to power law behaviour of long-range temporal correlations (LRTC). We perform this analysis for multiple channels and report similar behaviour. We find an increase in the long-term scaling exponent during seizure in all channels, which we attribute to an increase in persistent LRTC during seizure. The magnitude of the scaling exponent and its distribution in different channels can help in better identification of areas in brain most affected during seizure activity. The nature of epileptic seizures varies from patient-to-patient. To illustrate this, we report an increase in long-term scaling exponent for some patients which is also complemented by the recurrence plots (RP). RP is a graph that shows the time index of recurrence of a dynamical state. We perform Recurrence Quantitative analysis (RQA) and calculate RQA parameters like diagonal length, entropy, recurrence, determinism, etc. for ictal and interictal datasets. We find that the RQA parameters increase during seizure activity, indicating a transition. We observe that RQA parameters are higher during seizure period as compared to post seizure values, whereas for some patients post seizure values exceeded those during seizure. We attribute this to varying nature of seizure in different patients indicating a different route or mechanism during the transition. Our results can help in better understanding of the characterisation of epileptic EEG signals from a nonlinear analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detrended%20fluctuation" title="detrended fluctuation">detrended fluctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20range%20correlations" title=" long range correlations"> long range correlations</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrence%20plots" title=" recurrence plots"> recurrence plots</a> </p> <a href="https://publications.waset.org/abstracts/84822/analysis-of-epileptic-electroencephalogram-using-detrended-fluctuation-and-recurrence-plots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84822.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">176</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> 5-[Aryloxypyridyl (or Nitrophenyl)]-4H-1,2,4-Triazoles as Flexible Benzodiazepine Analogs: Synthesis, Receptor Binding Affinity and the Lipophilicity-Dependent Anti-Seizure Onset of Action</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Latifeh%20Navidpour">Latifeh Navidpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Shabnam%20Shabani"> Shabnam Shabani</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Heidari"> Alireza Heidari</a>, <a href="https://publications.waset.org/abstracts/search?q=Manouchehr%20Bashiri"> Manouchehr Bashiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Azadeh%20Ebrahim-Habibi"> Azadeh Ebrahim-Habibi</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Shahhosseini"> Soraya Shahhosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Shafaroodi"> Hamed Shafaroodi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayyed%20Abbas%20Tabatabai"> Sayyed Abbas Tabatabai</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20Toolabi"> Mahsa Toolabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new series of 5-(2-aryloxy-4-nitrophenyl)-4H-1,2,4-triazoles and 5-(2-aryloxy-3-pyridyl)-4H-1,2,4-triazoles, possessing C-3 thio or alkylthio substituents, was synthesized and evaluated for their benzodiazepine receptor affinity and anti-seizure activity. These analogues revealed similar to significantly superior affinity to GABAA/ benzodiazepine receptor complex (IC50 values of 0.04–4.1 nM), relative to diazepam as the reference drug (IC50 value of 2.4 nM). To determine the onset of anti-seizure activity, the time-dependent effectiveness of i.p. administration of compounds on pentylenetetrazole induced seizure threshold was studied and a very good relationship was observed between the lipophilicity (cLogP) and onset of action of studied analogues (r2 = 0.964). The minimum effective dose of the compounds, determined at the time the analogues showed their highest activity, was demonstrated to be 0.025–0.1 mg/kg, relative to diazepam (0.025 mg/kg). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=1" title="1">1</a>, <a href="https://publications.waset.org/abstracts/search?q=2" title="2">2</a>, <a href="https://publications.waset.org/abstracts/search?q=4-triazole" title="4-triazole">4-triazole</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20benzodiazepines" title=" flexible benzodiazepines"> flexible benzodiazepines</a>, <a href="https://publications.waset.org/abstracts/search?q=GABAA%2Fbezodiazepine%20receptor%20complex" title=" GABAA/bezodiazepine receptor complex"> GABAA/bezodiazepine receptor complex</a>, <a href="https://publications.waset.org/abstracts/search?q=onset%20of%20action" title=" onset of action"> onset of action</a>, <a href="https://publications.waset.org/abstracts/search?q=PTZ%20induced%20seizure%20threshold" title=" PTZ induced seizure threshold"> PTZ induced seizure threshold</a> </p> <a href="https://publications.waset.org/abstracts/136418/5-aryloxypyridyl-or-nitrophenyl-4h-124-triazoles-as-flexible-benzodiazepine-analogs-synthesis-receptor-binding-affinity-and-the-lipophilicity-dependent-anti-seizure-onset-of-action" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136418.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">104</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> Investigation of the EEG Signal Parameters during Epileptic Seizure Phases in Consequence to the Application of External Healing Therapy on Subjects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karan%20Sharma">Karan Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajay%20Kumar"> Ajay Kumar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG%20signal" title="EEG signal">EEG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=Reiki" title=" Reiki"> Reiki</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20consuming" title=" time consuming"> time consuming</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure" title=" epileptic seizure"> epileptic seizure</a> </p> <a href="https://publications.waset.org/abstracts/22441/investigation-of-the-eeg-signal-parameters-during-epileptic-seizure-phases-in-consequence-to-the-application-of-external-healing-therapy-on-subjects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22441.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">406</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> Schiff Bases of Isatin and Admantane-1-Carbohydrazide: Synthesis, Characterization, and Anticonvulsant Activity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hind%20O.%20Osman">Hind O. Osman</a>, <a href="https://publications.waset.org/abstracts/search?q=Tilal%20Elsaman"> Tilal Elsaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Bashir%20A.%20Yousef"> Bashir A. Yousef</a>, <a href="https://publications.waset.org/abstracts/search?q=Esraa%20Elhadi"> Esraa Elhadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aimun%20A.%20E.%20Ahmed"> Aimun A. E. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Eyman%20Mohamed%20Eltayib"> Eyman Mohamed Eltayib</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Suliman%20Mohamed"> Malik Suliman Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Magdi%20Awadalla%20Mohamed"> Magdi Awadalla Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is the most common neurological condition and cause of substantial morbidity and mortality. In the present study, the molecular hybridization tool was adopted to obtain six Schiﬀ bases of isatin and adamantane-1-carbohydrazide (18–23). Then, their anticonvulsant activity was evaluated using a pentylenetetrazole- (PTZ-) induced seizure model using phenobarbitone as a positive control. Our ﬁndings showed that compounds 18–23 provided signiﬁcant protection against PTZ-induced seizure, and maximum activities were associated with compound 23. Moreover, all investigated compounds increased the latency of induced convulsion and reduced the duration of epilepsy, with compound 23 being the best. Interestingly, most of the synthesized molecules showed a reduction in neurological symptoms and severity of the seizure. Molecular docking studies suggest GABA-A receptor as a potential target, and in silico ADME screening revealed that the pharmaceutical properties of compound 23 are within the speciﬁed limit. Thus, compound 23 was identified as a promising candidate that warrants further drug discovery processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=isatin%20and%20adamantane" title="isatin and adamantane">isatin and adamantane</a>, <a href="https://publications.waset.org/abstracts/search?q=anticonvulsant%20activity" title=" anticonvulsant activity"> anticonvulsant activity</a>, <a href="https://publications.waset.org/abstracts/search?q=PTZ-induced%20seizure" title=" PTZ-induced seizure"> PTZ-induced seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20docking" title=" molecular docking"> molecular docking</a> </p> <a href="https://publications.waset.org/abstracts/145055/schiff-bases-of-isatin-and-admantane-1-carbohydrazide-synthesis-characterization-and-anticonvulsant-activity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145055.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">207</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> Epileptic Seizure Onset Detection via Energy and Neural Synchronization Decision Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marwa%20Qaraqe">Marwa Qaraqe</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ismail"> Muhammad Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Erchin%20Serpedin"> Erchin Serpedin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20onset" title=" seizure onset"> seizure onset</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalography" title=" electroencephalography"> electroencephalography</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron" title=" neuron"> neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/24040/epileptic-seizure-onset-detection-via-energy-and-neural-synchronization-decision-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24040.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">477</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> From Electroencephalogram to Epileptic Seizures Detection by Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaetano%20Zazzaro">Gaetano Zazzaro</a>, <a href="https://publications.waset.org/abstracts/search?q=Angelo%20Martone"> Angelo Martone</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberto%20V.%20Montaquila"> Roberto V. Montaquila</a>, <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Pavone"> Luigi Pavone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20detection" title=" seizure detection"> seizure detection</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/109326/from-electroencephalogram-to-epileptic-seizures-detection-by-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109326.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">188</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 Lower Dose of Topiramate with Enough Antiseizure Effect: A Realistic Therapeutic Range of Topiramate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seolah%20Lee">Seolah Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoohyk%20Jang"> Yoohyk Jang</a>, <a href="https://publications.waset.org/abstracts/search?q=Soyoung%20Lee"> Soyoung Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Kon%20Chu"> Kon Chu</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20Kun%20Lee"> Sang Kun Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objective: The International League Against Epilepsy (ILAE) currently suggests a topiramate serum level range of 5-20 mg/L. However, numerous institutions have observed substantial drug response at lower levels. This study aims to investigate the correlation between topiramate serum levels, drug responsiveness, and adverse events to establish a more accurate and tailored therapeutic range. Methods: We retrospectively analyzed topiramate serum samples collected between January 2017 and January 2022 at Seoul National University Hospital. Clinical data, including serum levels, antiseizure regimens, seizure frequency, and adverse events, were collected. Patient responses were categorized as "insufficient" (reduction in seizure frequency <50%) or "sufficient" (reduction ≥ 50%). Within the "sufficient" group, further subdivisions included seizure-free and tolerable seizure subgroups. A population pharmacokinetic model estimated serum levels from spot measurements. ROC curve analysis determined the optimal serum level cut-off. Results: A total of 389 epilepsy patients, with 555 samples, were reviewed, having a mean dose of 178.4±117.9 mg/day and a serum level of 3.9±2.8 mg/L. Out of the samples, only 5.6% (n=31) exhibited insufficient response, with a mean serum level of 3.6±2.5 mg/L. In contrast, 94.4% (n=524) of samples demonstrated sufficient response, with a mean serum level of 4.0±2.8 mg/L. This difference was not statistically significant (p = 0.45). Among the 78 reported adverse events, logistic regression analysis identified a significant association between ataxia and serum concentration (p = 0.04), with an optimal cut-off value of 6.5 mg/L. In the subgroup of patients receiving monotherapy, those in the tolerable seizure group exhibited a significantly higher serum level compared to the seizure-free group (4.8±2.0 mg/L vs 3.4±2.3 mg/L, p < 0.01). Notably, patients in the tolerable seizure group displayed a higher likelihood of progressing into drug-resistant epilepsy during follow-up visits compared to the seizure-free group. Significance: This study proposed an optimal therapeutic concentration for topiramate based on the patient's responsiveness to the drug and the incidence of adverse effects. We employed a population pharmacokinetic model and analyzed topiramate serum levels to recommend a serum level below 6.5 mg/L to mitigate the risk of ataxia-related side effects. Our findings also indicated that topiramate dose elevation is unnecessary for suboptimal responders, as the drug's effectiveness plateaus at minimal doses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=topiramate" title="topiramate">topiramate</a>, <a href="https://publications.waset.org/abstracts/search?q=therapeutic%20range" title=" therapeutic range"> therapeutic range</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20dos" title=" low dos"> low dos</a>, <a href="https://publications.waset.org/abstracts/search?q=antiseizure%20effect" title=" antiseizure effect"> antiseizure effect</a> </p> <a href="https://publications.waset.org/abstracts/175540/a-lower-dose-of-topiramate-with-enough-antiseizure-effect-a-realistic-therapeutic-range-of-topiramate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175540.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">55</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> Auricular Electroacupuncture Rescued Epilepsy Seizure by Attenuating TLR-2 Inflammatory Pathway in the Kainic Acid-Induced Rats</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I-Han%20Hsiao">I-Han Hsiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Ping%20Huang"> Chun-Ping Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Liang%20Hsieh"> Ching-Liang Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi-Wen%20Lin"> Yi-Wen Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is chronic brain disorder that results in the sporadic occurrence of spontaneous seizures in the temporal lobe, cerebral cortex, and hippocampus. Clinical antiepileptic medicines are often ineffective or little benefits in the small amount of patients and usually initiate severe side effects. This inflammation contributes to enhanced neuronal excitability and the onset of epilepsy. Auricular electric-stimulation (AES) can increase parasympathetic activity and stimulate the solitary tract nucleus to induce the cholinergic anti-inflammatory pathway. Furthermore, it may be a therapeutic strategy for the treatment of epilepsy. In the present study, we want to investigate the effects of AES on inflammatory mediators in kainic acid (KA)-induced epileptic seizure rats. Experimental KA injection increased expression of TLR-2 pathway associated inflammatory mediators, were further reduced by either 2Hz or 15 Hz AES in the prefrontal cortex, hippocampus, and somatosensory cortex. We suggest that AES can successfully control the epileptic seizure by down-regulation of inflammation signaling pathway. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auricular%20electric-stimulation" title="auricular electric-stimulation">auricular electric-stimulation</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizures" title=" epileptic seizures"> epileptic seizures</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-inflammation" title=" anti-inflammation"> anti-inflammation</a> </p> <a href="https://publications.waset.org/abstracts/84898/auricular-electroacupuncture-rescued-epilepsy-seizure-by-attenuating-tlr-2-inflammatory-pathway-in-the-kainic-acid-induced-rats" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84898.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">185</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> ARIMA-GARCH, A Statistical Modeling for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salman%20Mohamadi">Salman Mohamadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Ali%20Tayaranian%20Hosseini"> Seyed Mohammad Ali Tayaranian Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Amindavar"> Hamidreza Amindavar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20prediction" title="epileptic seizure prediction ">epileptic seizure prediction </a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH%20and%20GARCH%20modeling" title=" ARCH and GARCH modeling"> ARCH and GARCH modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=heteroskedasticity" title=" heteroskedasticity"> heteroskedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/59028/arima-garch-a-statistical-modeling-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59028.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">406</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> Deep Learning Approaches for Accurate Detection of Epileptic Seizures from Electroencephalogram Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramzi%20Rihane">Ramzi Rihane</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Benayed"> Yassine Benayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title="electroencephalogram">electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure" title=" epileptic seizure"> epileptic seizure</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=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <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=seizure%20detection" title=" seizure detection"> seizure detection</a> </p> <a href="https://publications.waset.org/abstracts/193110/deep-learning-approaches-for-accurate-detection-of-epileptic-seizures-from-electroencephalogram-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193110.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">12</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> Epilepsy Seizure Prediction by Effective Connectivity Estimation Using Granger Causality and Directed Transfer Function Analysis of Multi-Channel Electroencephalogram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mona%20Hejazi">Mona Hejazi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Motie%20Nasrabadi"> Ali Motie Nasrabadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=effective%20connectivity" title="effective connectivity">effective connectivity</a>, <a href="https://publications.waset.org/abstracts/search?q=Granger%20causality" title=" Granger causality"> Granger causality</a>, <a href="https://publications.waset.org/abstracts/search?q=directed%20transfer%20function" title=" directed transfer function"> directed transfer function</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy%20seizure%20prediction" title=" epilepsy seizure prediction"> epilepsy seizure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/51480/epilepsy-seizure-prediction-by-effective-connectivity-estimation-using-granger-causality-and-directed-transfer-function-analysis-of-multi-channel-electroencephalogram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51480.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">469</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> Current and Emerging Pharmacological Treatment for Status Epilepticus in Adults</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mathew%20Tran">Mathew Tran</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepa%20Patel"> Deepa Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Breann%20Prophete"> Breann Prophete</a>, <a href="https://publications.waset.org/abstracts/search?q=Irandokht%20Khaki%20Najafabadi"> Irandokht Khaki Najafabadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Status epilepticus is a neurological disorder requiring emergent control with medical therapy. Based on guideline recommendations for adults with status epilepticus, the first-line treatment is to start a benzodiazepine, as they are quick at seizure control. The second step is to initiate a non-benzodiazepine anti-epileptic drug to prevent refractory seizures. Studies show that the anti-epileptic drugs are approximately equivalent in status epilepticus control once a benzodiazepine has been given. This review provides a brief overview of the management of status epilepticus based on evidence from the literature and evidence-based guidelines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neurological%20disorder" title="neurological disorder">neurological disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=status%20epilepticus" title=" status epilepticus"> status epilepticus</a>, <a href="https://publications.waset.org/abstracts/search?q=benzo%20diazepines" title=" benzo diazepines"> benzo diazepines</a>, <a href="https://publications.waset.org/abstracts/search?q=antiepileptic%20agents" title=" antiepileptic agents"> antiepileptic agents</a> </p> <a href="https://publications.waset.org/abstracts/148671/current-and-emerging-pharmacological-treatment-for-status-epilepticus-in-adults" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148671.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">120</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> Covid Encephalopathy and New-Onset Seizures in the Context of a Prior Brain Abnormality: A Case Report</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Sorour">Omar Sorour</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Leahy"> Michael Leahy</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Irvine"> Thomas Irvine</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Koren"> Vladimir Koren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Covid encephalitis is a rare yet dangerous complication, particularly affecting the older and immunocompromised. Symptoms range from confusion to delirium, coma, and seizures. Although neurological manifestations have become more well-characterized in COVID patients, little is known about whether priorneurological abnormalities may predispose patients to COVID encephalopathy. Case Description: A 73 y.o. male with a CT and MRI-confirmed stable, prior 9 mm cavernoma in the right frontal lobe and no past history of seizures was hospitalized with generalized weakness, abdominal pain, nausea, and shortness of breath with subsequent COVID pneumonia. Three days after the initial presentation, the patient developed a spontaneous generalized tonic-clonic seizure consistent with presumed COVID encephalitis, along with somnolence and confusion. A day later, the patient had two other seizure episodes. Follow-up EEG suggested an inter-ictal epileptic focus with sharp waves corresponding to roughly the same location as the patient’s pre-existing cavernoma. The patient’s seizures stopped shortly thereafter, while his encephalopathy continued for days. Conclusion: We illustrate that a pre-existing anatomic cortical abnormality may act as a potential nidus for new-onset seizure activity in the context of suggested COVID encephalopathy. Future studies may further demonstrate that manifestations of COVIDencephalopathy in certain patients may be more predictable than initially assumed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cavernoma" title="cavernoma">cavernoma</a>, <a href="https://publications.waset.org/abstracts/search?q=covid" title=" covid"> covid</a>, <a href="https://publications.waset.org/abstracts/search?q=encephalopathy" title=" encephalopathy"> encephalopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizures" title=" seizures"> seizures</a> </p> <a href="https://publications.waset.org/abstracts/138746/covid-encephalopathy-and-new-onset-seizures-in-the-context-of-a-prior-brain-abnormality-a-case-report" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138746.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">171</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> Spatiotemporal Propagation and Pattern of Epileptic Spike Predict Seizure Onset Zone</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Mohammadpour">Mostafa Mohammadpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Kapeller"> Christoph Kapeller</a>, <a href="https://publications.waset.org/abstracts/search?q=Christy%20Li"> Christy Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Josef%20Scharinger"> Josef Scharinger</a>, <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Guger"> Christoph Guger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Interictal spikes provide valuable information on electrocorticography (ECoG), which aids in surgical planning for patients who suffer from refractory epilepsy. However, the shape and temporal dynamics of these spikes remain unclear. The purpose of this work was to analyze the shape of interictal spikes and measure their distance to the seizure onset zone (SOZ) to use in epilepsy surgery. Thirteen patients' data from the iEEG portal were retrospectively studied. For analysis, half an hour of ECoG data was used from each patient, with the data being truncated before the onset of a seizure. Spikes were first detected and grouped in a sequence, then clustered into interictal epileptiform discharges (IEDs) and non-IED groups using two-step clustering. The distance of the spikes from IED and non-IED groups to SOZ was quantified and compared using the Wilcoxon rank-sum test. Spikes in the IED group tended to be in SOZ or close to it, while spikes in the non-IED group were in distance of SOZ or non-SOZ area. At the group level, the distribution for sharp wave, positive baseline shift, slow wave, and slow wave to sharp wave ratio was significantly different for IED and non-IED groups. The distance of the IED cluster was 10.00mm and significantly closer to the SOZ than the 17.65mm for non-IEDs. These findings provide insights into the shape and spatiotemporal dynamics of spikes that could influence the network mechanisms underlying refractory epilepsy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spike%20propagation" title="spike propagation">spike propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=spike%20pattern" title=" spike pattern"> spike pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=SOZ" title=" SOZ"> SOZ</a> </p> <a href="https://publications.waset.org/abstracts/176533/spatiotemporal-propagation-and-pattern-of-epileptic-spike-predict-seizure-onset-zone" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176533.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">63</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> Analysis of Biomarkers Intractable Epileptogenic Brain Networks with Independent Component Analysis and Deep Learning Algorithms: A Comprehensive Framework for Scalable Seizure Prediction with Unimodal Neuroimaging Data in Pediatric Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bliss%20Singhal">Bliss Singhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intractable%20epilepsy" title="intractable epilepsy">intractable epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</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=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20channels" title=" electroencephalogram channels"> electroencephalogram channels</a> </p> <a href="https://publications.waset.org/abstracts/164932/analysis-of-biomarkers-intractable-epileptogenic-brain-networks-with-independent-component-analysis-and-deep-learning-algorithms-a-comprehensive-framework-for-scalable-seizure-prediction-with-unimodal-neuroimaging-data-in-pediatric-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164932.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">84</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> Naïve Bayes: A Classical Approach for the Epileptic Seizures Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhaveek%20Maini">Bhaveek Maini</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20Dhanka"> Sanjay Dhanka</a>, <a href="https://publications.waset.org/abstracts/search?q=Surita%20Maini"> Surita Maini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20recognition" title="epileptic seizure recognition">epileptic seizure recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Na%C3%AFve%20Bayes" title=" Naïve Bayes"> Naïve Bayes</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/177537/naive-bayes-a-classical-approach-for-the-epileptic-seizures-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177537.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">61</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> Effect of Amlodipine on Dichlorvos-Induced Seizure in Mice</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omid%20Ghollipoor%20Bashiri">Omid Ghollipoor Bashiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Farzam%20Hatefi"> Farzam Hatefi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dichlorvos a synthetic organophosphate poisons are used as insecticide. These toxins can be used insecticides in agriculture and medicine for destruction and/or eradication of ectoparasites of animals. Studies have shown that Dichlorvos creation seizure effects in different animals. Amlodipine, dihydropyridine calcium channel blockers, widely used for treatment of cardiovascular diseases. Studies have shown that the calcium channel blockers are anticonvulsant effects in different animal models. The aim of this study was to determine the effect of Amlodipine on Dichlorvos-induced seizures in mice. In this experiment, the animals were received different doses of Amlodipine (2.5, 5, 10, 20 and 40 mg/ kg b.wt.) intraperitoneally 30 min before intraperitoneal injection of Dichlorvos (50 mg/kg b.wt). After Dichlorvos injection, clonic and tonic seizures, and finally was the fate was investigated. Results showed that Amlodipine dose-dependently reduced the severity of Dichlorvos-induced seizures, so that Amlodipine at a dose of 5mg (The lowest, p<0.05) and 40 mg/kg b.wt. (The highest, p<0.001) which had anticonvulsant effects. The anticonvulsant activity of Amlodipine suggests that possibly due to the antagonistic effect on voltage-dependent calcium channel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dichlorvos" title="dichlorvos">dichlorvos</a>, <a href="https://publications.waset.org/abstracts/search?q=amlodipine" title=" amlodipine"> amlodipine</a>, <a href="https://publications.waset.org/abstracts/search?q=seizures" title=" seizures"> seizures</a>, <a href="https://publications.waset.org/abstracts/search?q=mice" title=" mice "> mice </a> </p> <a href="https://publications.waset.org/abstracts/46575/effect-of-amlodipine-on-dichlorvos-induced-seizure-in-mice" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46575.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">306</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> Aphasia, Silence and the Non-Verbalisation of Performance (in Music)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Navonil%20Hazra">Navonil Hazra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper discusses how and why aphasia can be understood as the language of nonverbal communication in musical performance and also looks for the elements that are required to classify it as a nonverbal language. Since music is regarded as a nonverbal medium that cannot be engaged in any language, it is concerned about how aphasia might be called the language of nonverbalization. The paper also talks about how it portrays the magnificence of a performance, and how it expresses its likings or dislikes. Regarding the reasons for aphasia, the paper talks about the seizure factor and elucidates on seizure subjects as well. Furthermore, it discusses collective seizures and individual seizures. It also tries to consider aphasia as a-posteriori rather than a-priori looking at it from the lens of ‘Pure Reason’. Along with aphasia, the paper tries to make a critique of silence and the possibilities of looking at silence differently, also looking at the ontology of silence and sound. This paper also critically examines silence and the significance of gestures in performance. It also investigates whether gestures are accompanied by silence, establishing the notion of agential silence. This paper also talks about the place and role of memory in the formulation and analysis of a performance, as well as the plaguing and reclamation of memory, how memory alters the linear course of time and taunts us to look for alternative models of temporalities. This paper discusses the concept of 'auditory labour', with active and passive listening. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aphasia" title="aphasia">aphasia</a>, <a href="https://publications.waset.org/abstracts/search?q=gestures" title=" gestures"> gestures</a>, <a href="https://publications.waset.org/abstracts/search?q=memory" title=" memory"> memory</a>, <a href="https://publications.waset.org/abstracts/search?q=silence" title=" silence"> silence</a> </p> <a href="https://publications.waset.org/abstracts/185507/aphasia-silence-and-the-non-verbalisation-of-performance-in-music" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185507.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">42</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> Fast and Accurate Model to Detect Ictal Waveforms in Electroencephalogram Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piyush%20Swami">Piyush Swami</a>, <a href="https://publications.waset.org/abstracts/search?q=Bijaya%20Ketan%20Panigrahi"> Bijaya Ketan Panigrahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneh%20Anand"> Sneh Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Manvir%20Bhatia"> Manvir Bhatia</a>, <a href="https://publications.waset.org/abstracts/search?q=Tapan%20Gandhi"> Tapan Gandhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=ictal%20patterns" title=" ictal patterns"> ictal patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a> </p> <a href="https://publications.waset.org/abstracts/64484/fast-and-accurate-model-to-detect-ictal-waveforms-in-electroencephalogram-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64484.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">406</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> Case Report: Clinical Improvement of Forbrain Neurologic Signs in 3- Month- Old Persian Mastiff Dog with Calvarial Hyperostosis Syndrome after Corticosteroid, Antiepileptic and Antibiotic Therapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Jahani">Hamidreza Jahani</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Salehzadeh"> Zahra Salehzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Amini"> Ehsan Amini</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Tohidifar"> Mohsen Tohidifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Calvarial Hyperostosis Syndrome (CHS) is a benign bone disease of the skull. It is a non-neoplastic and proliferative bone disease, and the main feature of the disease is progressive and asymmetrical bone involvement. CHS is mostly reported in young male and female bullmastiff dogs and less frequently in other breeds. The etiology of CHS is unknown. This is the first case report of CHS in Iran. A 3-month-old male Persian Mastiff was presented with chief complaints of multiple episodes of seizure, pacing, bizarre behavior, delayed growth, head pressing, and difficulty in opening the mouth. Central blindness and open fontanelles were observed in clinical examination. No abnormality was found in the complete blood count and routine blood biochemical tests. CT scan findings include cortical thickening of frontal and parietal bones and enlargement of the left retropharyngeal lymph node. For treatment, oral clindamycin for two weeks, prednisolone and phenobarbital for one month, respectively, were administrated, and the case showed improvement after a week and recovered after one month. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=calvarial%20hyperostosis" title="calvarial hyperostosis">calvarial hyperostosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Persian%20Mastiff" title=" Persian Mastiff"> Persian Mastiff</a>, <a href="https://publications.waset.org/abstracts/search?q=frontal%20bone" title=" frontal bone"> frontal bone</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a> </p> <a href="https://publications.waset.org/abstracts/147402/case-report-clinical-improvement-of-forbrain-neurologic-signs-in-3-month-old-persian-mastiff-dog-with-calvarial-hyperostosis-syndrome-after-corticosteroid-antiepileptic-and-antibiotic-therapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147402.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">137</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">50</span> Depressive Symptoms in Children with Epilepsy Attending a Tertiary Care Hospital in Oman</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamood%20Al%20Kiyumi">Hamood Al Kiyumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salim%20Al%20Huseini"> Salim Al Huseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Al%20Risi"> Khalid Al Risi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Mirza"> Hassan Mirza</a>, <a href="https://publications.waset.org/abstracts/search?q=Amira%20Al%20Hosni"> Amira Al Hosni</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20Jaju"> Sanjay Jaju</a>, <a href="https://publications.waset.org/abstracts/search?q=Asaad%20Al%20Habsi"> Asaad Al Habsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objectives: The aim of this study was to assess the proportion of depressive symptoms along with demographic data in children diagnosed with epilepsy in a tertiary care institution in Oman. Methods: This cross-sectional study was conducted between June 2016 and August 2018. We have included 75 children with age group from five to 12 years old, attending epilepsy clinic at Sultan Qaboos University Hospital who were diagnosed with epilepsy and already on treatment. Patients were excluded if they have mental retardation. Validated Depression Scale for Children (CES-DC) questionnaire was utilized to assess the level of depressive symptoms among children. In addition, we have looked at associated factors including seizure status in the last three months, compliance with antiepileptic medications, type of epilepsy, and number of antiepileptic medications. Results: In this study, we found that depressive symptoms were present in 39 (52%) of patients. We also found that 96% of the patients were compliant to medications. In addition, seizure was present in the last three months in 48% of the sample studies. There was no statistically significant association between any of the studied variables and depression. Conclusions: Although depression is highly prevalent in children with epilepsy, this study did not find any significant association between the CES-DC scores and the studied factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=depression" title="depression">depression</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=Oman" title=" Oman"> Oman</a> </p> <a href="https://publications.waset.org/abstracts/104428/depressive-symptoms-in-children-with-epilepsy-attending-a-tertiary-care-hospital-in-oman" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104428.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">165</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">49</span> Lennox-gastaut Syndrome Associated with Dysgenesis of Corpus Callosum</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Bruce%20Janati">A. Bruce Janati</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Umair%20Khan"> Muhammad Umair Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Naif%20Alghassab"> Naif Alghassab</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Alzeir"> Ibrahim Alzeir</a>, <a href="https://publications.waset.org/abstracts/search?q=Assem%20Mahmoud"> Assem Mahmoud</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sammour"> M. Sammour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rationale: Lennox-Gastaut syndrome(LGS) is an electro-clinical syndrome composed of the triad of mental retardation, multiple seizure types, and the characteristic generalized slow spike-wave complexes in the EEG. In this article, we report on two patients with LGS whose brain MRI showed dysgenesis of corpus callosum(CC). We review the literature and stress the role of CC in the genesis of secondary bilateral synchrony(SBS). Method: This was a clinical study conducted at King Khalid Hospital. Results: The EEG was consistent with LGS in patient 1 and unilateral slow spike-wave complexes in patient 2. The MRI showed hypoplasia of the splenium of CC in patient 1, and global hypoplasia of CC combined with Joubert syndrome in patient 2. Conclusion: Based on the data, we proffer the following hypotheses: 1-Hypoplasia of CC interferes with functional integrity of this structure. 2-The genu of CC plays a pivotal role in the genesis of secondary bilateral synchrony. 3-Electrodecremental seizures in LGS emanate from pacemakers generated in the brain stem, in particular the mesencephalon projecting abnormal signals to the cortex via thalamic nuclei. 4-Unilateral slow spike-wave complexes in the context of mental retardation and multiple seizure types may represent a variant of LGS, justifying neuroimaging studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=Lennox-Gastaut%20syndrome" title=" Lennox-Gastaut syndrome"> Lennox-Gastaut syndrome</a>, <a href="https://publications.waset.org/abstracts/search?q=corpus%20callosum" title=" corpus callosum "> corpus callosum </a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a> </p> <a href="https://publications.waset.org/abstracts/31595/lennox-gastaut-syndrome-associated-with-dysgenesis-of-corpus-callosum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31595.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">446</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=PTZ-induced%20seizure&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PTZ-induced%20seizure&page=3">3</a></li> <li class="page-item"><a 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