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گواهی نمایه سازی مقاله Absence epilepsy seizure onsets detection based on ECG signal analysis

عنوان مقاله: Absence epilepsy seizure onsets detection based on ECG signal analysis
شناسه (COI) مقاله: ICBME20_091
منتشر شده در بیستمین کنفرانس مهندسی زیست پزشکی ایران در سال ۱۳۹۲
مشخصات نویسندگان مقاله:

Fatemeh Es.haghi - Microelectronic & Microsensor Lab,Electrical and Computer Engineering Department, University of Tabriz, Tabriz, Iran
Javad Frounchi - Microelectronic & Microsensor Lab,Electrical and Computer Engineering Department, University of Tabriz, Tabriz, Iran
Parviz Shahabi - School of Advanced Medical Science,Tabriz University of Medical Sciences, Tabriz, Iran
Mina Sadighi - School of Advanced Medical Science,Tabriz University of Medical Sciences, Tabriz, Iran

خلاصه مقاله:
Detecting epileptic seizure onsets is the main goal of numerous studies, since it has many profits for patients and clinicians. Methods based on electroencephalogram (EEG), electrocardiogram (ECG), and other electrophysiological signals had been used for automatic detection in the literature. For the first time, absence seizures have been detected based on ECG signals in this study. Animal models of absence epilepsy, WAG/Rij rats, with repetitive seizures (duration about few seconds’), have been investigated. After detecting QRS complexes from ECG signal and extracting 38 different linear, nonlinear and frequency domain features from heart rate variability, feature vectors were constructed. In order to obtain high efficiency detection algorithm, feature selection have been implemented based on wrapper approach. Results related to support vector machine (SVM), linear discriminate analysis (LDA), and k-nearest neighbor (kNN), three important classifiers for seizure detection have been compared in this work. The test results for patient- independent detection with 5 selected features in leave-one-out (LOO) train approach had accuracy of 74%, 72% and 71% for SVM, LDA and kNN, respectively. All the algorithms and methods have been optimized to be useful in embedded implementations

کلمات کلیدی:
Epilepsy, Absence, Seizure detection, ECG, Heart rate, Feature extraction, Classification

صفحه اختصاصی مقاله و دریافت فایل کامل: https://www.civilica.com/Paper-ICBME20-ICBME20_091.html