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Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation.
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An unbiased, efficient sleep–wake detection algorithm for a population with sleep disorders: change point decoder
Abstract Study Objectives The usage of wrist-worn wearables to detect sleep–wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep–wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep. Methods Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD’s performance was compared with the performance of the OA in relation to PSG. Results On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of −22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep–wake transitions with an error of 64.4. The OA method’s performance was 28.6 min, −0.03%, and −17.2, respectively. Conclusions The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep–wake transitions. The CPD could be used as an alternate framework to investigate sleep–wake dynamics within the conventional time frame of 30-s epochs.
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- Award ID(s):
- 1636933
- PAR ID:
- 10202433
- Date Published:
- Journal Name:
- Sleep
- Volume:
- 43
- Issue:
- 8
- ISSN:
- 0161-8105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/sleep/zsaa011
