Classification of Multichannel Ecg Signals Using a Cross-distance Analysis
نویسندگان
چکیده
This paper presents a multi-stage algorithm for multichannel ECG beat classification into normal and abnormal categories using a sequential beat clustering and a crossdistance analysis algorithm. After clustering stage, a search algorithm is applied to detect the main normal class. Then other clusters are classified based on their distance from the main normal class. The algorithm is developed for both 1-lead and 2lead ECG. Evaluated results on MIT-BIH database exhibit a classification error of less than 1% for 1-lead and 0.2% for 2lead and clustering error of 0.2%.
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