Electrocardiogram data mining based on frame classification by dynamic time warping matching

Gong Zhang, Witold Kinsner and Bin Huang

Computer Methods in Biomechanics and Biomedical Engineering, 2009, vol. 12, issue 6, 701-707

Abstract: This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.

Date: 2009
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DOI: 10.1080/10255840902882158

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