Detection of Cardiac Arrhythmia from ECG Using CNN and XGBoost

Abstract

Abnormal behaviour of the heart called arrhythmia can be recorded as an electrocardiogram (ECG) signal. ECG plays a vital role in the diagnosis of heart disease. Advances in machine learning allow the development of computer-aided diagnostic models to identify heart disease type. We have proposed a hybrid model with Convolution Neural Network (CNN) to auto extract features from ECG and use XGBoost to assess the type of arrhythmia. We tested our model to diagnosis eleven kinds of arrhythmia beats from the MIT-BIH arrhythmia database and obtained overall sensitivity of 92.61%, specificity of 99.85%, positive predictive value of 95.99% and accuracy of 99.84%. The robustness of the proposed model is further confirmed by classifying the arrhythmia beats into 5 classes according to the AAMI standard and comparing the results with the state-of-the-art methods. Attained overall Sensitivity of 94.36%, specificity of 99.44%, positive predicative value of 96.40% and Accuracy of 99.69% for 5 AAMI Classes. The results demonstrated that the proposed model could be used in the diagnosis of arrhythmia