کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
381253 1437493 2008 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
چکیده انگلیسی

The aim of this study is to evaluate the diagnostic accuracy of the support vector machines (SVMs) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. Post-ictal heart rate oscillations were studied in a heterogeneous group of patients with partial epilepsy. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem, and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features, which well represent the ECG signals, and the SVMs trained on these features achieved high classification accuracies (total classification accuracy was 99.44%).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 21, Issue 8, December 2008, Pages 1196–1203
نویسندگان
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