ArticleFrontiers in physiology2022
Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- HEART-Watch: A multimodal physiological dataset from a Google Pixel Watch across different physical states.PLOS digital health · 2026Article
- Global-key region collaborative noise-aware for electrocardiogram signal quality assessment.PloS one · 2026Article
- A Paralleled Multi-Task Learning-Based Framework for Single-Lead ECG Fine-Grained Noise Localization, Denoising and Signal Quality Assessment.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
As the fast development of wearable devices and Internet of things technologies, real-time monitoring of ECG signals is quite critical for cardiovascular diseases. However, dynamic ECG signals recorded in free-living conditions suffered from extremely serious noise pollution. Presently, most algorithms for ECG signal evaluation were designed to divide signals into acceptable and unacceptable. Such classifications were not enough for real-time cardiovascular disease monitoring. In the study, a wearable ECG quality database with 50,085 recordings was built, including A/B/C (or high quality/medium quality/low quality) three quality grades (A: high quality signals can be used for CVD detection; B: slight contaminated signals can be used for heart rate extracting; C: heavily polluted signals need to be abandoned). A new SQA classification method based on a three-layer wavelet scattering network and transfer learning LSTM was proposed in this study, which can extract more systematic and comprehensive characteristics by analyzing the signals thoroughly and deeply. Experimental results (
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