Cardiac arrhythmia classification from multilead ECG using multiscale non-linear analysis

In this paper, a novel technique Is proposed for detecting cardiac arrhythmias using signals obtained from a multi-lead electrocardiogram (ECG). The method employs the use of two non-linear features namely detrended fluctuation analysis and sample entropy. The features are calculated on signals obtained after performing discrete wavelet transform on the incoming raw ECG data and selecting the diagnostically relevant sub-bands. The DFC and SE features of the sub-band signal are computed and the performance of these features is evaluated using multilayer perceptron (MLP), radial basis function neural network (RBFNN) and probabilistic neural network (PNN) classifiers. The experimental result shows that, the combination of DFC and SE features along-with MLP classifier has a high accuracy value of 98.76%.