ECG Analysis Using Wavelet Transform, Neural Networks and Support Vector Machines, Application to Myocardial Ischemia Detection
Publish place: Conference on Electrical Engineering and Sustainable Development with a focus on new achievements in electrical engineering
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
EOESD01_230
تاریخ نمایه سازی: 11 خرداد 1393
Abstract:
In this paper, we propose a novel method for the detection of myocardial ischemic events from electrocardiogram (ECG) signal, using the Discrete Wavelet Transform (DWT) technique, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The ST-T Segment is obtained based on the detection of R peak location based on the well-known Pan-Tompkins method. Then ratio of energy in the DWT approximation coefficients rather than detail coefficients calculated as the features. ANN and SVM is used to build classifiers for ischemic and normal ECG signals. The proposed method achieved good performance in correct rate, sensitivity and specificity.
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Authors
Alireza Fallahi
Biomedical Engineering Department, Hamedan University of Technology, Hamedan, Iran
Razieh Jafari
Electrical Engineering Department, Hamedan University of Technology, Hamedan, Iran
Masoud Vejdannik
Electrical Engineering Department, Iran University of Science and Technology, Tehran, Iran