Electrocardiogram data mining based on frame classification by dynamic time warping matching.
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.
Zhang
Gong
G
St Boniface General Hospital, Winnipeg, Canada. umzhan00@hotmail.com
Kinsner
Witold
W
Huang
Bin
B
D004562:Electrocardiography; D016247:Information Storage and Retrieval; D008962:Models, Theoretical