Abstract
A novel scheme for real time detection of ischemic features from long term electrocardiograms (ECG), based on the dilated discrete Hermite expansion is proposed. The discrete Hermite functions used for the expansion are eigenvectors of a symmetric tridiagonal matrix that commutes with the centered Fourier matrix. The ECG signals were expanded in terms of Hermite functions using a simple dot product. The resulting coefficients were found to have details about the shape of the ECG signal. The first 50 coefficients had all sufficient information to reconstruct the ECG signal with acceptable percentage RMS difference (PRD). A committee neural network classifier with these 50 input parameters was trained to identify ischemic features, namely ST segment and T wave changes. A sensitivity of 98% and a specificity of 97.3% were achieved. A comparison of these figures with other contemporary classification schemes revealed a superior performance.
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