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

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  • Gong Zhang
  • Witold Kinsner
  • Bin Huang

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.

Suggested Citation

  • Gong Zhang & Witold Kinsner & Bin Huang, 2009. "Electrocardiogram data mining based on frame classification by dynamic time warping matching," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 12(6), pages 701-707.
    • Handle: RePEc:taf:gcmbxx:v:12:y:2009:i:6:p:701-707
    DOI: 10.1080/10255840902882158
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    Cited by:

    1. Lin Chu & Chong Huang & Qingsheng Liu & Chongfa Cai & Gaohuan Liu, 2019. "Spatial Heterogeneity of Winter Wheat Yield and Its Determinants in the Yellow River Delta, China," Sustainability, MDPI, vol. 12(1), pages 1-21, December.

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