TL;DR
This paper introduces a cross-modality knowledge distillation framework that leverages EEG data to significantly improve ECG-based sleep staging accuracy, reducing the need for obtrusive EEG setups.
Contribution
The study proposes a novel deep learning approach using knowledge distillation from EEG to ECG to enhance sleep staging performance with single-lead ECG.
Findings
Achieved 13.40% improvement in weighted-F1-score for 3-class sleep staging.
Achieved 14.30% improvement in weighted-F1-score for 4-class sleep staging.
Demonstrated feasibility of cross-modality knowledge transfer for sleep staging enhancement.
Abstract
An electroencephalogram (EEG) signal is currently accepted as a standard for automatic sleep staging. Lately, Near-human accuracy in automated sleep staging has been achievable by Deep Learning (DL) based approaches, enabling multi-fold progress in this area. However, An extensive and expensive clinical setup is required for EEG based sleep staging. Additionally, the EEG setup being obtrusive in nature and requiring an expert for setup adds to the inconvenience of the subject under study, making it adverse in the point of care setting. An unobtrusive and more suitable alternative to EEG is Electrocardiogram (ECG). Unsurprisingly, compared to EEG in sleep staging, its performance remains sub-par. In order to take advantage of both the modalities, transferring knowledge from EEG to ECG is a reasonable approach, ultimately boosting the performance of ECG based sleep staging. Knowledge…
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Taxonomy
MethodsKnowledge Distillation
