چکیده مقاله
Abnormal changes in the ST segment of an electrocardiogram ECG are very important diagnostic parameters for detecting myocardial ischemia Myocardial Ischemia is one of the most common causes of death in the world and early diagnosis and treatment of it has a critical importance In this study, we propose a novel method for the automatic detection of myocardial ischemic events from the electrocardiogram ECG signal The ECG analysis algorithm developed in this study consists of the Discrete Wavelet Transform DWT , statistical signal processing PCA & ICA and Artificial Neural Networks ANN The ST T segment is obtained based on the detection of R peak location, based on the well known Pan & Tompkins method A neural network based system was designed for automatic detection of myocardial ischemia The preprocessing stage feature extraction comprises Wavelet transform and statistical signal processing techniques Principal Component Analysis and Independent Component Analysis aiming at extracting discriminant information and reduce redundancy in the set of features Through the proposed system, classification efficiencies of ~99% were achieved and the misclassification of signatures was almost eliminated
کلیدواژهها
نویسندگان
Masoud Vejdannik
School of Electrical Engineering, Iran University of Science & Technology IUST , Narmak, Tehran ۱۶۸۴۴, Iran
شیوه ارجاع
Vejdannik, Masoud,1394,Automated Detection of Myocardial Ischemia Using Statistical Signal Processing and Neural Networks,International Conference on New Research Findings in Electrical Engineering and Computer Science,Tehran
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