A classification approach for myocardial infarction using voltage features extracted from four standard ECG leads

This paper, deals with classification of Anteroseptal Myocardial Infarction and normal subjects. Multiresolution approach based extraction of diagnostic pathological features from V1–V4 chest leads is proposed. Mahalanobish distance based classification is used for classification and generation of discriminant function.The digitized ECG signals is subjected to DWT based denoising before applying feature extraction technique. A multiresolution approach along with an adaptive thresholding is used for the detection of R - peaks. Then Q, S peak, QRS onset and offset points are identified. Finally, the T wave is detected. By detecting the baseline of the ECG data, height of R, Q, S and T wave are calculated. Computed QRS vector and T wave amplitude are used for classification of the two classes. Mahalanobish distance based classification method is used for finding discrimant functions for leads V1–V4 and analysis is made accordingly. For R-peak detection, proposed algorithm yields sensitivity and positive predictivity of 99.8% and 99.7% respectively with MIT BIH Arrhythmia database, 99.84% and 99.98% respectively with PTB diagnostic ECG database. For time plane features, an average coefficient of variation of 3.21 is obtained over 150 leads tested from PTB data, each with 10000 samples. Classification accuracy for this method is 96.4%.