Morphological Classification of ST segment using Reference STs Set

Morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. An abnormal ST segment change especially is a very important for finding myocardial ischemia. Long-term ECG recording is needed because an ST change is transient. Accordingly, physicians try to find the transient change of the ST segment. The aim of this study is to classify ST according to its shape type using a polynomial approximation method and the reference STs set. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The first step of feature point detection is the detection of QRS complex, and this is accomplished using the morphological characteristics of QRS complex such as the steep slope and high amplitude. The other feature points are also detected using their morphological characteristics. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes between the reference ST type and the least square curve. We applied the developed algorithm to the ECG data in European ST database. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.