Key result
Under-pillow sleep monitoring system detects sleep stages with ~62% accuracy compared to polysomnography.
Why the study?
Does an under-pillow unperturbed sleep monitoring system accurately identify sleep stages based on heart rate variability in healthy subjects?
Does an under-pillow unperturbed sleep monitoring system accurately identify sleep stages based on heart rate variability in healthy subjects?
An under-pillow piezoelectric sensor using EEMD and hidden Markov modeling demonstrates feasibility for non-contact sleep staging with >60% accuracy.
Premature for clinical sleep staging; leaves open validation of under-pillow HRV methods in larger, diverse cohorts.
Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1-S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.
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Xiang et al. (2018) studied Healthy individuals without sleep disorders (n=10). Under-pillow unperturbed sleep monitoring system (piezoelectric sensor) vs. Polysomnography (PSG) was evaluated on Accuracy of capturing sleep stages S1-S4. The unperturbed sleep monitoring system using an under-pillow sensor captured sleep stages S1-S4 with an average accuracy of 61.51% compared to polysomnography.
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