Key result
Singular value decomposition for ECG compression achieves ~96% arrhythmia classification accuracy for efficient remote monitoring.
Why the study?
Tele-cardiology ECG monitoring systems require compressed signals for transmission and storage, but previous work prioritized high-quality decompressed signals rather than evaluating arrhythmia classification performance after lossy compression with high compression ratios.
Answers are grounded in this paper and cite their sources.
SVD-based lossy compression of ECG signals maintains high accuracy (>96%) for arrhythmia classification using CNN and SVM, potentially improving real-time remote ECG monitoring.
Electrocardiogram (ECG) monitoring systems are widely applied to tele-cardiology healthcare programs nowadays, where ECG signals should always be compressed first during its transmission and storage. Previous studies attempted to achieve high quality decompressed signal with compression ratio as high as possible. In this paper, we investigated the performance on ECG arrhythmia classification on ECG signal decompressed after lossy compression with a high compression ratio. We proposed a simple but efficient method utilizing singular value decomposition (SVD) to decompose ECG signals, then applied the decompressed data to a convolutional neural network (CNN) and supporting vector machine (SVM) for classification. Using the optimization method with accuracy and compression ratio as objective functions, the highest average accuracy obtained is above 96% when the selected number of singular value is only 3. The evaluation results illustrated that the decompressed ECG signal even with a relatively high distortion can still achieve a satisfying performance in the arrhythmia classification.Thus,we proved that the real-time nature of the remote mobile ECG monitoring system can be greatly improved and countless people who are in need of ECG diagnosis can benefit from it.
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Zheng et al. (2021) studied ECG arrhythmia. Singular value decomposition (SVD) and CNN/SVM classification was evaluated on Average accuracy of ECG arrhythmia classification. Singular value decomposition for ECG compression achieved an average arrhythmia classification accuracy above 96% using only 3 singular values, demonstrating efficiency for remote monitoring.
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