A Morphological Classification Method of ECG ST-Segment Based on Curvature Scale Space

Abstract

Anomalous 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.

Classification of ST segment in ECG signals based on cross correlated supervised data

“…Thus, J point detection is more accurate than the other existing method [30]. On the other hand, for detecting ST segment end point most of the methods used time domain value were 5-60 ms counting from J point [28][29][30]. But in our proposed method we have used T wave pattern for detecting T wave start point that is, ST segment end point.…”

“…This study [28] selects the ST segment from J point to 60 ms and has classified the ST Segment based on their amplitude value. Curvature Scale Space based ST segment classification is also developed [29]. This study [29] shows 91.60% accuracy for small amount of ECG data.…”

“…Curvature Scale Space based ST segment classification is also developed [29]. This study [29] shows 91.60% accuracy for small amount of ECG data. It eliminates the endpoint detection of the ST segment.…”

“…In addition, in previous study [27][28][29][30] ST segment is classified based on their amplitude value in mV or μV. In our proposed method we used cross-correlation process where ST segment amplitude value is not mandatory.…”

“…In this case our proposed method works correctly. Previous works [27][28][29][30] use short term ECG signal most of them are less than 20 min. But in our proposed method we used minimum 30-min durations ECG signal and maximum 1-h episode.…”