Key result
A data-driven quality indicator using an unsupervised deep learning auto-encoder performed consistently strong across ECG quality assessment tasks compared to traditional indicators.
Why the study?
Existing ECG signal quality indicators rely on human-specified properties of clean signals, which inherently limits their performance.
Does an unsupervised deep learning model (auto-encoder) improve ECG quality assessment compared to traditional human-defined indicators?
Does an unsupervised deep learning model (auto-encoder) improve ECG quality assessment compared to traditional human-defined indicators?
An unsupervised deep learning auto-encoder provides a consistently strong, data-driven approach to ECG quality assessment, removing reliance on expert-specified properties.
Data-driven ECG quality assessment may enhance signal interpretation; leaves open prospective validation before clinical adoption.
OBJECTIVE: Noise and disturbances hinder effective interpretation of recorded ECG. To identify the clean parts of a recording, free from such disturbances, various quality indicators have been developed. Previous instances of these indicators focus on human-defined desirable properties of a clean signal. The reliance on human-specified properties places an inherent limitation on the potential power of signal quality indicators. To move away from this limitation, we propose a data-driven quality indicator. METHODS: We use an unsupervised deep learning model, the auto-encoder, to derive the quality indicator. For different quality assessment settings we compare the performance of our quality indicator with traditional indicators. RESULTS: The data-driven method performs consistently strong across tasks while performance of the traditional indicators varies strongly from task to task. CONCLUSION: This strong performance indicates the potential of data-driven quality indicators for use in ECG processing, removing the reliance on expert-specified desirable properties. SIGNIFICANCE: The proposed methodology can easily be extended towards learning quality indicators in other data modalities.
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Seeuws et al. (2021) studied ECG quality assessment. Unsupervised deep learning model (auto-encoder) vs. Traditional indicators was evaluated on Performance across different quality assessment settings. A data-driven quality indicator using an unsupervised deep learning auto-encoder performed consistently strong across ECG quality assessment tasks compared to traditional indicators.
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