Aomalous changes in the ST
segment, including ST level deviation and ST shape change, are the major
parameters in clinical electrocardiogram (ECG) diagnosis of myocardial
ischemia. Automatic detection of ST segment morphology can provide a more
accurate evidence for clinical diagnosis of myocardial ischemia. In this paper,
we proposed a method for classifying the shape of the ST-segment based on the
curvature scale space (CSS) technique. First, we established a reference ST set
and preprocessed the ECG signal by using the CSS technique. Then, the corner
points in the ST-segment were detected at a high scale of the CSS and tracked
through multiple lower scales, in order to improve its localization. Finally,
the current beat of ST morphology can be distinguished by the corner points. We
applied the developed algorithm to the ECG recordings in European ST-T database
and QT database to validate the accuracy of the algorithm. The experimental
results showed that the average detection accuracy of our algorithm was 91.60%.
We could conclude that the proposed method is able to provide a new way for the
automatic detection of myocardial ischemia.
Cite this paper
Hu, F. , Li, C. , Zhang, Y. , Jin, D. , Ma, Z. , Sun, Y. , Wang, J. (2015) A Morphological Classification Method of ECG ST-Segment Based on Curvature Scale Space. Journal of Biosciences and Medicines
, 38-43. doi: 10.4236/jbm.2015.39006
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