Why the study?
Does a deep learning model using ECG and respiratory effort accurately determine sleep stages in a large heterogeneous population?
Does a deep learning model using ECG and respiratory effort accurately determine sleep stages in a large heterogeneous population?
Deep learning applied to ECG and respiratory effort can provide sleep staging information, offering an alternative when EEG is unavailable or infeasible.
May offer EEG alternative for sleep staging; hypothesis-generating and should not yet change practice.
Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.
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Sun et al. (2019) studied this question.
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