ECG arrhythmia classification using a 2-D convolutional neural network

TL;DR

This paper introduces a novel 2-D CNN approach for ECG arrhythmia classification, transforming ECG signals into images and achieving high accuracy without manual feature extraction, validated on the MIT-BIH database.

Contribution

The study presents a deep 2-D CNN model that classifies ECG arrhythmias directly from images, outperforming traditional methods and well-known CNN architectures.

Findings

Achieved 99.05% accuracy on MIT-BIH dataset

Validated robustness with 10-fold cross-validation

Performed comparably or better than AlexNet and VGGNet

Abstract

In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into a two-dimensional grayscale image as an input data for the CNN classifier. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. In addition, we compared our proposed classifier with two well-known CNN models; AlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were used for the evaluation of the classifier. As a result, our classifier achieved 99.05% average accuracy with 97.85% average sensitivity. To precisely validate our CNN classifier, 10-fold cross-validation was performed at the evaluation which involves every ECG recording as a test data. Our experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent classification accuracy without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.