Electrocardiogram Quality Assessment Using Unsupervised Deep Learning

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.