Key result
A semi-supervised ResNet model with confidence-level-based training achieved an average ECG classification accuracy of 90.2% on data from a novel wearable patch, outperforming conventional methods by 5.4%.
Why the study?
Holter surveillance causes discomfort and inconvenience, and existing deep learning models struggle to differentiate supraventricular premature beats from atrial fibrillation in newly obtained wireless ECG patch datasets.
Does a confidence-level-based ResNet deep learning model improve the accuracy of arrhythmia classification from a wearable ECG patch compared to conventional models?
Does a confidence-level-based ResNet deep learning model improve the accuracy of arrhythmia classification from a wearable ECG patch compared to conventional models?
Answers are grounded in this paper and cite their sources.
A novel wearable ECG patch combined with a confidence-level-based ResNet model achieved 90.2% accuracy in classifying arrhythmias from noisy real-world data, outperforming conventional deep learning models.
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.
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Wang et al. (2022) studied Arrhythmia (n=62). ResNet with confidence-level-based training vs. Conventional ECG classification methods (e.g., CNN, LSTM) was evaluated on Average classification accuracy. A semi-supervised ResNet model with confidence-level-based training achieved an average ECG classification accuracy of 90.2% on data from a novel wearable patch, outperforming conventional methods by 5.4%.
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