Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

TL;DR

This paper presents a deep neural network that performs both classification and self-supervised regression on ECG signals to detect arrhythmias and improve signal quality, leveraging unlabeled and labeled data for enhanced performance.

Contribution

The study introduces a combined deep learning model for simultaneous ECG signal classification and regression, utilizing self-supervised learning to reduce dependence on labeled data.

Findings

Achieved 87.33% accuracy in arrhythmia classification.

Demonstrated effective ECG signal approximation with high quality.

Improved classification accuracy to 87.78% by transferring knowledge from regression.

Abstract

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87:33% and balanced accuracy of 80:54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87:78%.