ECG frame classification using dynamic time warping

This paper presents an electrocardiogram (ECG) frame classification technique realized by a dynamic time warping (DTW) matching techniques which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar nonstationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. 4 threshold is setup for DWT 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-minute ECG recording.