Denoising and classification of Arrhythmia using MEMD and ANN

One of the major reasons of death worldwide is Cardiovascular diseases. One of its type is Arrhythmia in which normal rhythm of heart is varied due to damage in heart muscles and electrolyte imbalance. To study cardiovascular disease, Electrocardiogram (ECG) signal is used that plays a significant role in identifying Arrhythmia. By using a combination of Multivariate Empirical Mode Decomposition (MEMD) and Artificial Neural Network (ANN), a hybrid technique is proposed in this research work to detect and classify Arrhythmia. MEMD, a promising technique that is used nowadays for denoising of multichannel signals, is used in this work. Two main features, i.e., RR interval and Heart Rate are extracted from the ECG signal for the detection of Arrhythmia. Using these features, Arrhythmia is classified into Tachycardia and Bradycardia. Classification is done using pattern recognition of ANN in which Multilayer feed forward neural network is used and trained through Back Propagation Algorithm. Results that are achieved using proposed technique is compared with previous existing techniques as in 2016 (Discrete Wavelet Transform) DWT and ANN gave 87.01% and in 2018 (Empirical Mode Decomposition) EMD and (Linear Discriminant Analysis) LDA gave 87% accuracy. As compare to these techniques, the proposed technique results show that it is an efficient technique that gives better accuracy of 89.8%.