A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection

TL;DR

This paper introduces a wireless ECG patch and a semi-supervised deep learning approach that significantly improves real-time cardiovascular disease detection accuracy over traditional methods.

Contribution

It presents a novel wireless ECG device combined with a semi-supervised CNN-LSTM framework for better heartbeat classification in real-time.

Findings

Achieved an average accuracy of 90.2% in heartbeat classification.

Outperformed conventional ECG classification methods by 5.4%.

Demonstrated effectiveness of semi-supervised learning on poorly labeled data.

Abstract

Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the individuals who carry them. We developed a new wireless ECG patch in this work and applied a deep learning framework based on the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models. However, we find that the models using the existing techniques are not able to differentiate two main heartbeat types (Supraventricular premature beat and Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy of 58.0 %. We proposed a semi-supervised method to process the badly labelled data samples with using the confidence-level-based training. The experiment results conclude that the proposed method can approach an average accuracy of 90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification methods.