A multi-feature based morphological algorithm for ST shape classification

Abnormal ST segment is an important parameter for the diagnosis of myocardial ischemia and other heart diseases. As most abnormal ST segments sustain for only a few seconds, it is impractical for the doctors to detect and classify abnormal ones manually on time. Even though many ST segment classification algorithms are proposed to meet the rising demand of automatic myocardial ischemia diagnosis, they are often with lower recognition rate. The aim of this study is to detect abnormal ST segments precisely and classify them into more categories, and thus provide more detailed category information to help the clinicians make decisions. This study sums up ten common abnormal ST segments according to the clinical ECG records and proposes a morphological classification algorithm of ST segment based on multi-features. This algorithm consists of two parts: Feature points extraction and ST segment classification. In the first part, R wave is detected by using the 2B-spline wavelet transform, and mode-filtering method and morphological characteristics are used for other feature points extraction. In the ST segment classification process, ST segment level, variance, slope value, number of convex/concave points and other feature parameters are employed to classify the ST segment. This algorithm can classify abnormal ST segments into ten categories above. We evaluated the performance of the proposed algorithm based on ECG data in the European ST-T database. The global recognition rate of 92.7% and the best accuracy of 97% demonstrated the effectiveness of the proposed solution.