Dissimilarity factor based classification of inferior myocardial infarction ECG

Electrocardiography (ECG) is popular non-invasive technique for preliminary level investigation on cardiovascular assessment. Computerized analysis of ECG can significantly contribute towards assisted diagnosis and in early detection of many cardiac diseases. Conventional automated ECG classifiers employing soft computing tools may suffer from the inaccuracies that may result in different clinical feature extraction stages. In this paper, we propose the use of a statistical index, namely, dissimilarity factor (D) for classification of normal and Inferior Myocardial Infarction (IMI) data, without the need of any direct clinical feature extraction. Time aligned ECG beats were obtained through filtering, wavelet decomposition processes, followed by PCA based beat enhancement to generate multivariate time series data. The T wave and QRS segments of IMI datasets from Lead II, III and aVF were extracted and compared with corresponding segments of healthy patients using Physionet ptbdb data. With 35 IMI datasets, the average composite dissimilarity factor Dc between normal data was found to be 0.39, and the same between normal and abnormal data were found to be 0.65. This paper shows the promise of descriptive statistical tools as an alternative for medical signal analysis.