Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

Automatic sleep stage classification is an important paradigm in computational intelligence and promises considerable advantages to the health care. Most current automated methods require the multiple electroencephalogram (EEG) channels and typically cannot distinguish the S1 sleep stage from EEG. The aim of this study is to revisit automatic sleep stage classification from EEGs using complexity science methods. The proposed method applies fuzzy entropy and permutation entropy as kernels of multi-scale entropy analysis. To account for sleep transition, the preceding and following 30 seconds of epoch data were used for analysis as well as the current epoch. Combining the entropy and spectral edge frequency features extracted from one EEG channel, a multi-class support vector machine (SVM) was able to classify 93.8% of 5 sleep stages for the SleepEDF database [expanded], with the sensitivity of S1 stage was 49.1%. Also, the Kappa's coefficient yielded 0.90, which indicates almost perfect agreement.

ObjectivesDetecting sleep latency during the Multiple Sleep Latency Test (MSLT) using electroencephalogram (scalp-EEG) is time-consuming. The aim of this study was to evaluate the efficacy of a novel in-ear sensor (in-ear EEG) to detect the sleep latency, compared to scalp-EEG, during MSLT in healthy adults, with and without sleep restriction.MethodsWe recruited 25 healthy adults (28.5±5.3 years) who participated in two MSLTs with simultaneous recording of scalp and in-ear EEG. Each test followed a randomly assigned sleep restriction (≤5 hours sleep) or usual night sleep (≥7 hours sleep). Reaction time and Stroop test were used to assess the functional impact of the sleep restriction. The EEGs were scored blind to the mode of measurement and study conditions, using American Academy of Sleep Medicine 2012 criteria. The Agreement between the scalp and in-ear EEG was assessed using Bland-Altman analysis.ResultsTechnically acceptable data were obtained from 23 adults during 69 out of 92 naps in the sleep restriction condition and 25 adults during 85 out of 100 naps in the usual night sleep. Meaningful sleep restrictions were confirmed by an increase in the reaction time (mean ± SD: 238±30 ms vs 228±27 ms; P=0.045). In the sleep restriction condition, the in-ear EEG exhibited a sensitivity of 0.93 and specificity of 0.80 for detecting sleep latency, with a substantial agreement (κ=0.71), whereas after the usual night’s sleep, the in-ear EEG exhibited a sensitivity of 0.91 and specificity of 0.89, again with a substantial agreement (κ=0.79).ConclusionThe in-ear sensor was able to detect reduced sleep latency following sleep restriction, which was sufficient to impair both the reaction time and cognitive function. Substantial agreement was observed between the scalp and in-ear EEG when measuring sleep latency. This new in-ear EEG technology is shown to have a significant value as a convenient measure for sleep latency.

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PurposeCurrent evidence of whether napping promotes or declines cognitive functions among older adults is contradictory. The aim of this study was to determine the association between nap duration and cognitive functions among Saudi older adults.MethodsOld adults (> 60 years) were identified from the Covid-19 vaccine center at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia between May and August 2021. Face-to-face interviews were conducted by a geriatrician or family physicians. Data collected for each participant included sociodemographic, sleep patterns, health status and cognitive functions. St. Louis University mental status (SLUMS) was used to assess the cognitive functions. A multi-Linear regression model was used to determine the association between cognitive functions and nap duration.ResultsTwo-hundred participants (58 females) aged 66 ± 5 years were recruited. Participants were categorized according to their nap duration into non-nappers (0 min), short nappers (> 0- ≤ 30 min), moderate nappers (> 30–≤ 90 min), and extended nappers (> 90 min). The mean duration of the nap was 49.1 ± 58.4 min. The mean SLUMS score was 24.1 ± 4.7 units. Using the multi-linear regression model, the mean total SLUMS score for extended nappers was, on average, significantly lower than non-nappers [−2.16 units; 95% CI (−3.66, −0.66), p = < 0.01] after controlling for the covariates (age, sex, education level, sleep hours, diabetes mellitus, hypertension, pain).ConclusionsExtended napping was associated with deterioration in cognitive function among Saudi older adults.

Background and Objectives: Changes in autonomic cardiac activity during night sleep are well documented. However, there is limited information regarding changes in the autonomic cardiac profile during daytime naps. Heart rate variability (HRV) and baroreflex sensitivity (BRS) are reliable measures of autonomic cardiac activity. The purpose of this study was to determine the changes in HRV and BRS during daytime naps in healthy men. Methods: This was a cross-sectional study of 25 healthy men. Polysomnographic recording with electrocardiogram monitoring was conducted for all volunteers during a 50-80 min nap between 3.30 pm and 5.30 pm. Five-minute segments during pre-nap wakefulness, non-rapid eye movement (NREM) sleep stages (N1, N2, and N3), rapid eye movement (REM) sleep stage, and post-nap wakefulness were used to measure changes in the variation in HRV parameters, including inter-beat interval (RR-interval), total spectral power (TP), highfrequency power (HF), low-frequency power (LF), and low frequency/high-frequency ratio (LF/HF). BRS was also measured for 10 min during pre-and post-nap wakefulness using finger arterial pressure measurement (Finometer Pro ®). Results: HRV increased significantly during NREM sleep compared with that during prenap wakefulness (p < 0.05), as reflected by RR-interval prolongation, higher HF, and increased HF nu (normalized units). Furthermore, there was a parallel reduction in TP, LF, and LF/HF ratio during NREM sleep, indicating parasympathetic predominance over cardiac autonomic activity. HF and HF nu were significantly reduced during REM sleep compared with that during NREM sleep (p < 0.05). BRS did not show significant differences between pre-and post-nap wakefulness. Conclusion: We observed a progressive increase in parasympathetic activity during daytime sleep as NREM sleep deepened compared with that during wakefulness and REM sleep. Daytime nap may have a favorable cardiovascular impact.

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MOTIVATION: Studies have shown poor clinical effectiveness of the Epworth Sleepiness Scale (ESS) due to its ambiguity of items and cultural applicability. This study aimed to investigate the efficacy of a Visual Analog Scale (VAS) to assess sleepiness, compared to ESS. METHODS: Thirty-two obstructive sleep apnea (OSA) patients and 32 healthy participants completed two visits, 1 month apart, during which they completed both ESS and VAS. Patients diagnosed with OSA were treated with Continuous positive airway pressure (CPAP) between visits. The agreement between the ESS and VAS scores in both patients with OSA and healthy participants was investigated using Pearson correlation and Area Under the receiver operating characteristics. RESULTS: The (mean ± standard deviation) Oxygen Desaturation Index for patients with OSA was 18.5 ± 5.7 events/hour and 1.7 ± 1.0 events/hour in the healthy participants. A reduction in sleepiness, following CPAP treatment occurred in patients with OSA, using the ESS (11.2 ± 5.5–4.7 ± 5.0 points, P < 0.001) and the VAS (50.2 ± 3.0–21.9 ± 26.5 mm, P < 0.001). There was no significant change in sleepiness, in healthy participants using the ESS (3.91 ± 3.14–3.34 ± 3.27 points ( P < 0.48) or the VAS (15.58 ± 21.21–12.05 ± 14.75 mm, ( P < 0.44). A Likert scale showed that the VAS was easier to use compared to ESS in visit 1 (VAS: 8.7 ± 1.9 points, ESS: 7.7 ± 2.6 points, ( P < 0.001), and visit 2 (VAS: 9.5 ± 1.4 points, ESS: 8.6 ± 1.5 points, P < 0.001). CONCLUSION: These preliminary results suggest that the VAS can detect a change in sleepiness after CPAP treatment in patients with OSA and that the VAS was also easier to use compared to ESS.