Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis

Cardiac beat classification is a key process in the detection of myocardial ischemic episodes in the electrocardiographic signal. In the present study, we propose a multicriteria sorting method for classifying the cardiac beats as ischemic or not. Through a supervised learning procedure, each beat is compared to preclassified category prototypes under five criteria. These criteria refer to ST segment changes, T wave alterations, and the patientʹs age. The difficulty in applying the above criteria is the determination of the required method parameters, namely the thresholds and weight values. To overcome this problem, we employed a genetic algorithm, which, after proper training, automatically calculates the optimum values for the above parameters. A task-specific cardiac beat database was developed for training and testing the proposed method using data from the European Society of Cardiology ST-T database. Various experimental tests were carried out in order to adjust each module of the classification system. The obtained performance was 91% in terms of both sensitivity and specificity and compares favorably to other beat classification approaches proposed in the literature.