Classification of Arrhythmia From ECG Signals Using CSL-Net

Liang, Hongrui; Song, Ge; Xu, Chuanbin; Deng, Xiupeng; Li, Yang; Dou, Shengyao; Chen, Da

doi:10.1109/itoec53115.2022.9734447

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Related works [40][41][42][43][44][45][46][47][48][49][50] and other works have shown that ResNet18 [51], SE-ECGNet [52] and ECGNet [53] are three models commonly used in the field of ECG signal detection. This paper compares the performance of the proposed model with three commonly used deep learning models for arrhythmia classification from several dimensions, including model prediction accuracy, recall rate, and F1 score.…”

Section: Test Of Ecg Denoising Algorithmmentioning

confidence: 99%

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis

Luo,

Zhang,

et al. 2024

Biosensors

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Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

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