Detection of arrhythmia from the analysis of ECG signal using artificial neural networks

Abstract

Arrhythmia is a heart rhythm problem that could indicate a symptom of heart disease that often contributes to the increase in hospitalization in many developed countries. The patient of heart disease requires continuous monitoring and close attention to their vital sign such as the heart rate. There are many attempts to automate the detection of Arrhythmia from the Electrocardiogram (ECG) readings of patient. Nevertheless, the accuracy of some of these methods isnot satisfactory and prone to biased result due to inter-patient variations of ECG dataset.The purpose of this research addresses the arrhythmia classification problem from the ECG signal using Artificial Neural Network (ANN). First, we perform feature extraction on the ECG data which are the four features from RR intervals. The features are then transformed into a feature vector. Then we modelled sixteen different models of ANN where four different algorithms were used such as Bayesian Regularization (BR), Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Resilient Backpropagation (RP). The sixteen models are built withadifferent number of neurons in the hidden layer. We used the dataset from Massachusetts Institutes of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database for evaluating our models which are simulated in MATLAB. The results of the simulation were analyzedand the best model was compared with the previous work. The analysis of our research indicates that the ANN usingBayesian regularization withtwenty number of neurons in the hidden layer is the optimal model compared to other models with an overall accuracy of 83.1%. The Normal class Sensitivity was 97.4%, Specificity of 66.7% and Positive Predictive Value of 77.1%. The SVEB Sensitivity was 60% with Specificity of 86.9% and Positive Predictive Value of 42.9%. The VEB Sensitivity was 66.7% with Specificity of 88.7% and Positive Predictive Value of 66.7%. The comparison with other worksindicatesthat our model outperforms the previous work in terms of sensitivity and overall accuracy.