Document Type
Article
Version Deposited
Published Version
Publication Date
11-20-2023
Publication Title
Frontiers in Big Data
DOI
10.3389/fdata.2023.1270756
Abstract
Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches. Copyright © 2023 Vu, Petty, Yakut, Usman, Xue, Haas, Hirsh and Zhao.
Recommended Citation
Vu T, Petty T, Yakut K, Usman M, Xue W, Haas FM, Hirsh RA and Zhao X (2023) Real-time arrhythmia detection using convolutional neural networks. Front. Big Data 6:1270756. doi: 10.3389/fdata.2023.1270756
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
© 2023 Vu, Petty, Yakut, Usman, Xue, Haas, Hirsh and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).