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.