Detection of cardiac ailments from multilead ECG using diagnostic eigen error features

Accurate detection of life-threatening cardiac ailments is one of the important task in monitoring patient's health. In this paper, a new method for detection and classification of cardiac ailments from multilead electrocardiogram (MECG) is presented. The singular value decomposition (SVD) is used to convert the MECG data matrix into two unitary matrices (eigen matrices) and one diagonal matrix. According to clinical importance, first few atoms from the eigen matrices are selected. The root mean square error (RMSE) between the unitary matrices of both template MECG and analyzed MECG are used as diagnostic eigen error (DEE) features. The combination of singular values of analyzed MECG and DEE features are used as input to the least square support vector machine (LSSVM) classifier. The LSSVM detect the cardiac ailments such as myocardial infarction and hypertrophy. An average accuracy of 95.07% is found using LSSVM classifier with radial basis function (RBF) kernel and 5-fold cross-validation scheme.