Electrocardiogram Heartbeat Classification Using Convolutional Neural Networks for the Detection of Cardiac Arrhythmia

TL;DR

This paper presents a CNN-based method for automatic classification of ECG signals to detect arrhythmias, achieving over 95% accuracy, which can aid early diagnosis and treatment of heart diseases.

Contribution

The study introduces a customized CNN model for ECG arrhythmia classification, demonstrating high accuracy and effectiveness over traditional methods.

Findings

CNN achieved 95.2% accuracy in arrhythmia classification

The model's precision and recall are approximately 95% and 95.4%

Effective early detection of heart rhythm irregularities

Abstract

The classification of the electrocardiogram (ECG) signal has a vital impact on identifying heart-related diseases. This can ensure the premature finding of heart disease and the proper selection of the patient's customized treatment. However, the detection of arrhythmia is a challenging task to perform manually. This justifies the necessity of a technique for automatic detection of abnormal heart signals. Therefore, our work is based on the classification of five classes of ECG arrhythmic signals from Physionet's MIT-BIH Arrhythmia Dataset. Artificial Neural Networks (ANN) have demonstrated significant success in ECG signal classification. Our proposed model is a Convolutional Neural Network (CNN) customized to categorize the ECG signals. Our result testifies that the planned CNN model can successfully categorize arrhythmia with an overall accuracy of 95.2%. The average precision and recall of the proposed model are 95.2% and 95.4%, respectively. This model can effectively be used to detect irregularities of heart rhythm at an early stage.