Efficient Algorithm for Early Detection of Myocardial Ischemia using PCA based Features

Objective: The purpose of this work is to develop an efficient algorithm for uncovering the myocardial ischemia at early stages from ECG signal using Principal Component Analysis (PCA). Methods/Statistical Analysis: The proposed work mainly involves three stages namely denoising, extracting features and classification. The removal of noise from ECG signal is achieved by applying wavelet threshold technique. The extraction of clinically useful features is carried out by selecting ST-T complex from ECG beat samples followed by dimensionality reduction using PCA. These features are fed to MLP, SVM and KNN classifier models for diagnosing myocardial ischemia at early stages. The performance of classifier models are validated with ECG data obtained from physiobank database in terms of performance measures such as classification accuracy, sensitivity and positive prediction accuracy. Findings: The comparisons of experimental results have shown that the MLP classifier model has great scope for diagnosing myocardial ischemia at early stages. The MLP classifier model has resulted in classification accuracy of 90.51%, PPA of 93.8% and sensitivity of 96.19%. Application/Improvements: The proposed PCA based method has shown an improved accuracy of 90.51% in comparison with classifiers developed by other researchers.