LAUSR

Automated and accurate detection of myocardial infarction (MI) is a momentous task because of its association with damage of heart muscles and sudden cardiac arrest. This work highlights the investigation of discriminative feature representations to distinguish MI episodes from normal sinus rhythms by combining the strength of boosted classification approach. Proposed method analyzes the spectral coherency of 12 lead electrocardiogram (ECG) signal to explore the discrimination of normal and MI data in cross spectral domain. Coherence spectrum reveals the distinguishing characteristics of the pathologically varying ECG signals which in turn identify and localize the MI episode. In this view, two new feature metrics namely spectral coherence indices (SCIs) and phase coherence indices (PCIs) are introduced to capture the significant coherence variations. The effectiveness of the extracted features is determined by the Fisher score (F-score) estimation. Finally, a boosting framework using the ensemble of support vector machine (SVM) classifiers is presented to improve the performances of the proposed detection model. The boosted SVM augments the boosting technique by introducing a new cluster-based training sample set selection algorithm. The proposed detection model is empirically evaluated using the open access Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. The accuracy of 98.96% and 98.85% is obtained for detecting and identifying the different types of MI pathology, respectively. Comparative study with the existing work exhibits encouraging performances with reduced feature dimension.