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 scalp-EEG. 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.

Objectives: Detecting 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. Methods: We 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. Results: Technically 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). Conclusion: The 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.

Sleep monitoring with wearable electroencephalography (EEG) has recently been validated and reported in the research community. One such device is our ultra-wearable, unobtrusive, and inconspicuous in-ear EEG system, which has already been demonstrated to be next-generation solution for out-of-clinic sleep monitoring. We here provide a further proof of concept of the utility of ear-EEG in day time drowsiness monitoring in the real-world. For rigour, hypnograms are obtained from manually scored daytime nap recordings from twentythree subjects, while a complexity science feature-structural complexity extracted from scalp- and ear-EEG recordings - is used in the classification stage, in conjunction with a binary-class support vector machine (SVM). The achieved drowsiness classification accuracies range from 80.0% to 82.9% for ear-EEG, with the corresponding accuracies for scalp-EEG ranging from 86.8 % to 88.8 %. Given the notoriously difficult to classify drowsiness related changes in EEG (similar to the issues with the NREM Stage 1), this conclusively confirms the feasibility of in-ear EEG for automatic light sleep classification. This also promises a key stepping stone towards continuous, discreet, and user-friendly wearable out-of-clinic drowsiness monitoring in the real-world, with numerous applications in the monitoring the state of body and mind of pilots, train drivers, and tele-operators.

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.

Measuring pH has become a major key for determining health conditions, and food safety. The traditional pH assessment approaches are costly and offer low sensitivity. Here, a novel pH sensor based on a pH-responsive hydrogel has been developed. A Fresnel lens pattern was replicated on the surface of the pH-responsive hydrogel using the replica mould method. The pH sensors were tested in a pH range of 4–7. Introducing various pH solutions to the pH sensor led to volumetric shifts as the hydrogel swelled with pH. Consequently, the dimensions of the replicated Fresnel lens changed, modifying the focal length and the focus efficiency of the optical sensor. As a result, the measured optical power at a fixed distance from the sensor changed with pH. The optical sensor showed the best performance in the acidic region when pH changed from 4.5 to 5.5, in which the recorded power increased by 13%. The sensor exhibited high sensitivity to pH changes with a short respond time in a reversible manner. The developed pH optical sensor may have applications in medical point-of-care diagnostics and wearable continuous pH detection devices.

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), high-frequency 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 pre-nap 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.