ECG Heartbeat Classification: A Deep Transferable Representation

TL;DR

This paper introduces a deep learning approach for ECG heartbeat classification that not only accurately identifies five arrhythmias but also transfers learned knowledge to classify myocardial infarction, demonstrating high accuracy on multiple datasets.

Contribution

It presents a novel deep convolutional neural network method capable of transfer learning between arrhythmia and myocardial infarction classification tasks.

Findings

Achieved 93.4% accuracy in arrhythmia classification.

Achieved 95.9% accuracy in myocardial infarction classification.

Demonstrated effective transfer of knowledge between ECG classification tasks.

Abstract

Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.