From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

OBJECTIVE: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community.

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults.

Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.

Correction: Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study.

[This corrects the article DOI: 10.2196/46338.].

Method to determine whether sleep phenotypes are driven by endogenous circadian rhythms or environmental light by combining longitudinal data and personalised mathematical models.

Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure along with social constraints and behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised interventions. The timing of the human sleep-wake cycle has been modelled as an interaction of a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure and the natural light-dark cycle generated by the Earth's rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate that individual sleep phenotypes in each of 34 older participants (65-83y) can be described by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights that different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology.

Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study.

BACKGROUND: Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. OBJECTIVE: This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. METHODS: Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. RESULTS: The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21). CONCLUSIONS: These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.

International e-Delphi Consensus Recommendations for the Assessment and Diagnosis of Circadian rest-Activity Rhythm Disorders (CARDs) in Patients with Cancer.

PURPOSE: Circadian rest-Activity Rhythm Disorders (CARDs) are common in patients with cancer, particularly in advanced disease. CARDs are associated with increased symptom burden, poorer quality of life, and shorter survival. Research and reporting practices lack standardization, and formal diagnostic criteria do not exist. This electronic Delphi (e-Delphi) study aimed to formulate international recommendations for the assessment and diagnosis of CARDs in patients with cancer. METHODS: An international e-Delphi was performed using an online platform (Welphi). Round 1 developed statements regarding circadian rest-activity rhythms, diagnostic criteria, and assessment techniques. Rounds 2 and 3 involved participants rating their level of agreement with the statements and providing comments until consensus (defined internally as 67%) and stability between rounds were achieved. Recommendations were then created and distributed to participants for comments before being finalized. RESULTS: Sixteen participants from nine different clinical specialties and seven different countries, with 5-35 years of relevant research experience, were recruited, and thirteen participants completed all three rounds. Of the 164 generated statements, 66% achieved consensus, and responses were stable between the final two rounds. CONCLUSIONS: The e-Delphi resulted in international recommendations for assessing and diagnosing CARDs in patients with cancer. These recommendations should ensure standardized research and reporting practices in future studies.

Contactless and longitudinal monitoring of nocturnal sleep and daytime naps in older men and women: a digital health technology evaluation study.

STUDY OBJECTIVES: To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. METHODS: We collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD). RESULTS: Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary. CONCLUSIONS: CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.

The impact of wavelength on acute non-visual responses to light: A systematic review and meta-analysis.

Light is detected in the eye by three classes of photoreceptors (rods, cones, and intrinsically photosensitive retinal ganglion cells (ipRGCs)) that are each optimized for a specific function and express a particular light-detecting photopigment. The significant role of short-wavelength light and ipRGCs in improving alertness has been well-established; however, few reviews have been undertaken to assess the other wavelengths' effects regarding timing and intensity. This study aims to evaluate the impact of different narrowband light wavelengths on subjective and objective alertness among the 36 studies included in this systematic review, 17 of which were meta-analyzed. Short-wavelength light (∼460-480 nm) significantly improves subjective alertness, cognitive function, and neurological brain activities at night, even for a sustained period (∼6h) (for λmax: 470/475 nm, 0.4 < |Hedges's g| < 0.6, p < 0.05), but except early morning, it almost does not show this effect during the day when melatonin level is lowest. Long-wavelength light (∼600-640 nm) has little effect at night, but significantly increases several measures of alertness at lower irradiance during the daytime (∼1h), particularly when there is homeostatic sleep drive (for λmax: ∼630 nm, 0.5 < |Hedges's g| < 0.8, p < 0.05). The results further suggest that melanopic illuminance may not always be sufficient to measure the alerting effect of light.

Machine learning estimation of human body time using metabolomic profiling.

Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.

Evaluating the Empatica E4 Derived Heart Rate and Heart Rate Variability Measures in Older Men and Women.

Wearable heart rate monitors offer a cost-effective way of non-invasive, long-term monitoring of cardiac health. Validation of wearable technologies in an older populations is essential for evaluating their effectiveness during deployment in healthcare settings. To this end, we evaluated the validity of heart rate measures from a wearable device, Empatica E4, and compared them to the electrocardiography (ECG). We collected E4 data simultaneously with ECG in thirty-five older men and women during an overnight sleep recording in the laboratory. We propose a robust approach to resolve the missing inter-beat interval (IBI) data and improve the quality of E4 derived measures. We also evaluated the concordance of heart rate (HR) and heart rate variability (HRV) measures with ECG. The results demonstrate that the automatic E4 heart rate measures capture long-term HRV whilst the short-term metrics are affected by missing IBIs. Our approach provides an effective way to resolve the missing IBI issue of E4 and extracts reliable heart rate measures that are concordant with ECG. Clinical Relevance- This work discusses data quality challenges in heart rate data acquired by wearables and provides an efficient and reliable approach for extracting heart rate measures from the E4 wearable device and validates the metrics in older adults.

Effects of the selective orexin-2 receptor antagonist JNJ-48816274 on sleep initiated in the circadian wake maintenance zone: a randomised trial.

The effects of orexinergic peptides are diverse and are mediated by orexin-1 and orexin-2 receptors. Antagonists that target both receptors have been shown to promote sleep initiation and maintenance. Here, we investigated the role of the orexin-2 receptor in sleep regulation in a randomised, double-blind, placebo-controlled, three-period crossover clinical trial using two doses (20 and 50 mg) of a highly selective orexin-2 receptor antagonist (2-SORA) (JNJ-48816274). We used a phase advance model of sleep disruption where sleep initiation is scheduled in the circadian wake maintenance zone. We assessed objective and subjective sleep parameters, pharmacokinetic profiles and residual effects on cognitive performance in 18 healthy male participants without sleep disorders. The phase advance model alone (placebo condition) resulted in disruption of sleep at the beginning of the sleep period compared to baseline sleep (scheduled at habitual time). Compared to placebo, both doses of JNJ-48816274 significantly increased total sleep time, REM sleep duration and sleep efficiency, and reduced latency to persistent sleep, sleep onset latency, and REM latency. All night EEG spectral power density for both NREM and REM sleep were unaffected by either dose. Participants reported significantly better quality of sleep and feeling more refreshed upon awakening following JNJ-48816274 compared to placebo. No significant residual effects on objective performance measures were observed and the compound was well tolerated. In conclusion, the selective orexin-2 receptor antagonist JNJ-48816274 rapidly induced sleep when sleep was scheduled earlier in the circadian cycle and improved self-reported sleep quality without impact on waking performance.

Characterizing the modern light environment and its influence on circadian rhythms.

Humans have largely supplanted natural light cycles with a variety of electric light sources and schedules misaligned with day-night cycles. Circadian disruption has been linked to a number of disease processes, but the extent of circadian disruption among the population is unknown. In this study, we measured light exposure and wrist temperature among residents of an urban area during each of the four seasons, as well as light illuminance in nearby outdoor locations. Daily light exposure was significantly lower for individuals, compared to outdoor light sensors, across all four seasons. There was also little seasonal variation in the realized photoperiod experienced by individuals, with the only significant difference occurring between winter and summer. We tested the hypothesis that differential light exposure impacts circadian phase timing, detected via the wrist temperature rhythm. To determine the influence of light exposure on circadian rhythms, we modelled the impact of morning and night-time light exposure on the timing of the maximum wrist temperature. We found that morning and night-time light exposure had significant but opposing impacts on maximum wrist temperature timing. Our results demonstrate that, within the range of exposure seen in everyday life, night-time light can delay the onset of the maximum wrist temperature, while morning light can lead to earlier onset. Our results demonstrate that humans are minimizing natural seasonal differences in light exposure, and that circadian shifts and disruptions may be a more regular occurrence in the general population than is currently recognized.

S-cone contribution to the acute melatonin suppression response in humans.

Light influences diverse aspects of human physiology and behaviour including neuroendocrine function, the circadian system and sleep. A role for melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) in driving such effects is well established. However, rod and/or cone signals routed through ipRGCs could also influence "non-visual" spectral sensitivity. In humans, this has been most extensively studied for acute, light-dependent, suppression of nocturnal melatonin production. Of the published action spectra for melatonin suppression, one demonstrates a spectral sensitivity consistent with that expected for melanopsin while our own (using briefer 30 minute light exposures) displays very high sensitivity to short wavelength light, suggesting a contribution of S-cones. To clarify that possibility, six healthy young male participants were each exposed to 30 minutes of five irradiances of 415 nm monochromatic light (1-40 µW/cm2 ) across different nights. These data were then combined with the original action spectrum. The aggregated data are incompatible with the involvement of any single-opsin and multi-opsin models based on the original action spectrum (including Circadian Stimulus) fail to predict the responses to 415 nm stimuli. Instead, the extended action spectrum can be most simply approximated by an ~2:1 combination of melanopsin and S-cone signals. Such a model also better describes the magnitude of melatonin suppression observed in other studies using an equivalent 30 minute mono- or polychromatic light paradigm but not those using longer (90 minute) light exposures. In sum, these data provide evidence for an initial S-cone contribution to melatonin suppression that rapidly decays under extended light exposure.

Living Without Temporal Cues: A Case Study.

Isolation from external time cues allows endogenous circadian rhythmicity to be demonstrated. In this study, also filmed as a television documentary, we assessed rhythmic changes in a healthy man time isolated in a bunker for 9 days/nights. During this period the lighting conditions were varied between: (1) self-selected light/dark cycle, (2) constant dim light, and (3) light/dark cycle with early wake up. A range of variables was assessed and related to the sleep-wake cycle, psychomotor and physical performance and clock-time estimation. This case study using modern non-invasive monitoring techniques emphasizes how different physiological circadian rhythms persist in temporal isolation under constant dim light conditions with different waveforms, free-running with a period (τ) between 24 and 25 h. In addition, a significant correlation between time estimation and mid-sleep time, a proxy for circadian phase, was demonstrated.

Effect of acute total sleep deprivation on plasma melatonin, cortisol and metabolite rhythms in females.

Disruption to sleep and circadian rhythms can impact on metabolism. The study aimed to investigate the effect of acute sleep deprivation on plasma melatonin, cortisol and metabolites, to increase understanding of the metabolic pathways involved in sleep/wake regulation processes. Twelve healthy young female participants remained in controlled laboratory conditions for ~92 hr with respect to posture, meals and environmental light (18:00-23:00 hr and 07:00-09:00 hr <8 lux; 23:00-07:00 hr 0 lux (sleep opportunity) or <8 lux (continuous wakefulness); 09:00-18:00 hr ~90 lux). Regular blood samples were collected for 70 hr for plasma melatonin and cortisol, and targeted liquid chromatography-mass spectrometry metabolomics. Timepoints between 00:00 and 06:00 hr for day 1 (baseline sleep), day 2 (sleep deprivation) and day 3 (recovery sleep) were analysed. Cosinor analysis and MetaCycle analysis were performed for detection of rhythmicity. Night-time melatonin levels were significantly increased during sleep deprivation and returned to baseline levels during recovery sleep. No significant differences were observed in cortisol levels. Of 130 plasma metabolites quantified, 41 metabolites were significantly altered across the study nights, with the majority decreasing during sleep deprivation, most notably phosphatidylcholines. In cosinor analysis, 58 metabolites maintained their rhythmicity across the study days, with the majority showing a phase advance during acute sleep deprivation. This observation differs to that previously reported for males. Our study is the first of metabolic profiling in females during sleep deprivation and recovery sleep, and offers a novel view of human sleep/wake regulation and sex differences.

Circadian regulation in human white adipose tissue revealed by transcriptome and metabolic network analysis.

Studying circadian rhythms in most human tissues is hampered by difficulty in collecting serial samples. Here we reveal circadian rhythms in the transcriptome and metabolic pathways of human white adipose tissue. Subcutaneous adipose tissue was taken from seven healthy males under highly controlled 'constant routine' conditions. Five biopsies per participant were taken at six-hourly intervals for microarray analysis and in silico integrative metabolic modelling. We identified 837 transcripts exhibiting circadian expression profiles (2% of 41619 transcript targeting probes on the array), with clear separation of transcripts peaking in the morning (258 probes) and evening (579 probes). There was only partial overlap of our rhythmic transcripts with published animal adipose and human blood transcriptome data. Morning-peaking transcripts associated with regulation of gene expression, nitrogen compound metabolism, and nucleic acid biology; evening-peaking transcripts associated with organic acid metabolism, cofactor metabolism and redox activity. In silico pathway analysis further indicated circadian regulation of lipid and nucleic acid metabolism; it also predicted circadian variation in key metabolic pathways such as the citric acid cycle and branched chain amino acid degradation. In summary, in vivo circadian rhythms exist in multiple adipose metabolic pathways, including those involved in lipid metabolism, and core aspects of cellular biochemistry.

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low-cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex-printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self-applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier ("random forests") and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter-individual variation in sleep parameters. The results demonstrate that machine-learning-based scoring of around-the-ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine-learning-based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine-learning-based scoring holds promise for large-scale sleep studies.

Effect of Single and Combined Monochromatic Light on the Human Pupillary Light Response.

The pupillary light reflex (PLR) is a neurological reflex driven by rods, cones, and melanopsin-containing retinal ganglion cells. Our aim was to achieve a more precise picture of the effects of 5-min duration monochromatic light stimuli, alone or in combination, on the human PLR, to determine its spectral sensitivity and to assess the importance of photon flux. Using pupillometry, the PLR was assessed in 13 participants (6 women) aged 27.2 ± 5.41 years (mean ± SD) during 5-min light stimuli of purple (437 nm), blue (479 nm), red (627 nm), and combinations of red+purple or red+blue light. In addition, nine 5-min, photon-matched light stimuli, ranging in 10 nm increments peaking between 420 and 500 nm were tested in 15 participants (8 women) aged 25.7 ± 8.90 years. Maximum pupil constriction, time to achieve this, constriction velocity, area under the curve (AUC) at short (0-60 s), and longer duration (240-300 s) light exposures, and 6-s post-illumination pupillary response (6-s PIPR) were assessed. Photoreceptor activation was estimated by mathematical modeling. The velocity of constriction was significantly faster with blue monochromatic light than with red or purple light. Within the blue light spectrum (between 420 and 500 nm), the velocity of constriction was significantly faster with the 480 nm light stimulus, while the slowest pupil constriction was observed with 430 nm light. Maximum pupil constriction was achieved with 470 nm light, and the greatest AUC0-60 and AUC240-300 was observed with 490 and 460 nm light, respectively. The 6-s PIPR was maximum after 490 nm light stimulus. Both the transient (AUC0-60) and sustained (AUC240-300) response was significantly correlated with melanopic activation. Higher photon fluxes for both purple and blue light produced greater amplitude sustained pupillary constriction. The findings confirm human PLR dependence on wavelength, monochromatic or bichromatic light and photon flux under 5-min duration light stimuli. Since the most rapid and high amplitude PLR occurred within the 460-490 nm light range (alone or combined), our results suggest that color discrimination should be studied under total or partial substitution of this blue light range (460-490 nm) by shorter wavelengths (~440 nm). Thus for nocturnal lighting, replacement of blue light with purple light might be a plausible solution to preserve color discrimination while minimizing melanopic activation.

Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study.

Electroencephalography (EEG) recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants, and laborious in application. There is a recognized need to develop more easily applicable yet reliable EEG systems that allow unobtrusive long-term recording of sleep-wake EEG ideally away from the laboratory setting. cEEGrid is a recently developed flex-printed around-the-ear electrode array, which holds great potential for sleep-wake monitoring research. It is comfortable to wear, simple to apply, and minimally intrusive during sleep. Moreover, it can be combined with a smartphone-controlled miniaturized amplifier and is fully portable. Evaluation of cEEGrid as a motion-tolerant device is ongoing, but initial findings clearly indicate that it is very well suited for cognitive research. The present study aimed to explore the suitability of cEEGrid for sleep research, by testing whether cEEGrid data affords the signal quality and characteristics necessary for sleep stage scoring. In an accredited sleep laboratory, sleep data from cEEGrid and a standard PSG system were acquired simultaneously. Twenty participants were recorded for one extended nocturnal sleep opportunity. Fifteen data sets were scored manually. Sleep parameters relating to sleep maintenance and sleep architecture were then extracted and statistically assessed for signal quality and concordance. The findings suggest that the cEEGrid system is a viable and robust recording tool to capture sleep and wake EEG. Further research is needed to fully determine the suitability of cEEGrid for basic and applied research as well as sleep medicine.