期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2012
卷号：1
期号：4
页码：517-520
出版社：Shri Pannalal Research Institute of Technolgy
摘要：ECG signal analysis for abnormalities detection using discrete wavelet transform and Back Propagation Neural Network is addressed in this paper. Proposed technique used to detect the abnormal ECG Sample and classify it into two different classes (normal and abnormal). We have employed MIT-BIH arrhythmia database and chosen 45 files of one minute recording where 25 files are considered as normal class and 20 files of abnormal class out of total 48 files. The features are break up in to two classes that are DWT based features and morphological feature of ECG signal which is an input to the classifier. Back Propagation Neural Network (BPNN) are employed to classify the ECG signal and the stem performance is measured on the basis of percentage accuracy. For the normal class sample 96% of accuracy is reached whereas 100% accuracy is achieved for normal class sample. The overall system accuracy obtained is 97.8 % using (BPNN) classifier.
