Autonomous Detection of Heartbeats and Categorizing them by using Support Vector Machines

سال انتشار: 1392
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 800

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شناسه ملی سند علمی:

ICBME20_092

تاریخ نمایه سازی: 25 فروردین 1394

چکیده مقاله:

In this paper a new method for categorizing 5 special types of heartbeats has been developed by use of time and apparent properties of the Wavelet Transform of the ECG signal.By using the method in this paper first each heart beat identified autonomously and important points and segments of it,were derived .Then expected features for categorizing the heartbeats are extracted. Finally we categorized the arrhythmias by using the Support Vector Machines. In order to train the SVMand for analyzing its accuracy; arrhythmic signals of MIT-BIH dataset have been used. The results which have been achieved bythis method also contain 96.67 percent of accuracy for categorizing five different heartbeats including Normal (N) LeftBundle Branch Block(LBBB), Right Bundle Branch Block(LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC).The advantage of using this method compared to the other ones is that we could achieve the expected precision by using less training attributes respect to the other methods

کلیدواژه ها:

arrhythmia ، categorizing ، ECG ، segmentation ، Support Vector Machine (SVM)

نویسندگان

Hassan Yazdanian

Department of Biomedical Engineering University of Isfahan Isfahan, Iran

Ashkan Nomani

Department of Biomedical Engineering University of Isfahan Isfahan, Iran

Mohammad Reza Yazdchi

Department of Biomedical Engineering University of Isfahan Isfahan, Iran