Abstract
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.
Highlights
Myocardial ischemia is a disorder of cardiac function
The ECG data in European ST-T database and QT database were used to evaluate the performance of the proposed algorithm
We proposed a ST shape classification method based on the curvature scale-space technique
Summary
Myocardial ischemia is a disorder of cardiac function. It occurs when the heart muscle, or myocardium, receives insufficient blood flow caused by a partial or complete blockage of the coronary arteries. A number of detection algorithms of ST segment change have been developed to detect ischemic episode, such as the artificial neural network [2] [3], the hidden Markov model [4], discrete wavelet transform (DWT) and support vector machine (SVM) [5]. Most of these algorithms focused on the detection of the ST level deviation and only a few methods directly classify the ST shape. Due to the influence of the noise and drift, the point of the peak curvature might not truly reflect the information of ST shape
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