Searching and mining trillions of time series subsequences under dynamic time warping
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Data mining a trillion time series subsequences under dynamic time warping
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Automatic real-time detection and classification of ECG patterns is of great importance in early diagnosis and treatment of life-threatening cardiac arrhythmia. In this paper, we have presented dynamic time warping (DTW) distance based approach for classification of arrhythmic ECG beats, with an aim of using it in smart-phone/mobile environment. The performance of the proposed method is tested on ECG beats of various arrhythmia types selected from MIT-BIH arrhythmia database. We have compared the proposed DTW approach using naive Bayes classifier with relative band spectral power as feature. The DTW approach has shown superior performance compared to the naive Bayes classifier. Furthermore, we have verified the performance of the DTW approach on down-sampled ECG beats in order to improve speed of the DTW algorithm. It is observed that the performance of the DTW approach did not deteriorate even after subsampling of ECG beats. The DTW with subsampling has been aimed at real-time arrhythmia detection in wearable mobile healthcare systems in telemedicine scenario for continuous monitoring of ECG records from cardiac patients.