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. 2023 Nov 20:6:1270756.
doi: 10.3389/fdata.2023.1270756. eCollection 2023.

Real-time arrhythmia detection using convolutional neural networks

Thong Vu  1 , Tyler Petty  1 , Kemal Yakut  2 , Muhammad Usman  2 , Wei Xue  2 , Francis M Haas  2 , Robert A Hirsh  3 , Xinghui Zhao  1
Affiliations

Real-time arrhythmia detection using convolutional neural networks

Thong Vu et al. Front Big Data. .

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.

Keywords: anomaly detection; big data; convolutional neural networks; machine learning; smart health.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
ECG signals of nine different types of heartbeat.
Figure 2
Figure 2
ECG to image conversion example.
Figure 3
Figure 3
The ECG detection workflow diagram.
Figure 4
Figure 4
Prototype of wearable heart monitoring device.
Figure 5
Figure 5
Distribution of the 360 Hz recording by confidence.
See this image and copyright information in PMC

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