Efficient ECG Beats Classification Techniques for The Cardiac Arrhythmia Detection Based on Wavelet Transformation
Unknown authors
Abstract
Arrhythmia is one of the cardiovascular disease types that affect humans and often leads to death.generally, ECG signals uses to diagnose the patient's heart state where the ECG illustrates the electrical activities and physiological state of the heart.This paper proposes ECG classification model to classify four types of heartbeats for the early detection of Arrhythmia.Detail wavelet coefficients of ECG were extracted using discrete wavelet transform (DWT) to produce new datasets of ECG with for the dimensions and processed information to ensure the efficiency of proposed classification techniques.In addition, power spectral density (PSD) has been calculated for approximate wavelet coefficients of ECG to extract more relevant features that improve the performance of the classifier.The two classifier models use, the convolution neural network (CNN) utilizes for deep learning networks with artificial neural network (ANN) and the Random forest ensemble method.The experiments and results show that, the proposed model with RF archives 98.5% classification accuracy considering all decomposition levels of DWT.Additionally, the solution with CNN-ANN achieves 96% classification accuracy at the third decomposition level.Therefore, the results show the impact of the proposed solution with high efficiency in terms of fewer dimensions and high accuracy.