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.

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.

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.

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.

Improved Sleep, Memory, and Cellular Pathological Features of Tauopathy, Including the NLRP3 Inflammasome, after Chronic Administration of Trazodone in rTg4510 Mice.

Several cellular pathways contribute to neurodegenerative tauopathy-related disorders. Microglial activation, a major component of neuroinflammation, is an early pathologic hallmark that correlates with cognitive decline, while the unfolded protein response (UPR) contributes to synaptic pathology. Sleep disturbances are prevalent in tauopathies and may also contribute to disease progression. Few studies have investigated whether manipulations of sleep influence cellular pathologic and behavioral features of tauopathy. We investigated whether trazodone, a licensed antidepressant with hypnotic efficacy in dementia, can reduce disease-related cellular pathways and improve memory and sleep in male rTg4510 mice with a tauopathy-like phenotype. In a 9 week dosing regimen, trazodone decreased microglial NLRP3 inflammasome expression and phosphorylated p38 mitogen-activated protein kinase levels, which correlated with the NLRP3 inflammasome, the UPR effector ATF4, and total tau levels. Trazodone reduced theta oscillations during rapid eye movement (REM) sleep and enhanced REM sleep duration. Olfactory memory transiently improved, and memory performance correlated with REM sleep duration and theta oscillations. These findings on the effects of trazodone on the NLRP3 inflammasome, the unfolded protein response and behavioral hallmarks of dementia warrant further studies on the therapeutic value of sleep-modulating compounds for tauopathies.SIGNIFICANCE STATEMENT Dementia and associated behavioral symptoms such as memory loss and sleep disturbance are debilitating. Identifying treatments that alleviate symptoms and concurrently target cellular pathways contributing to disease progression is paramount for the patients and their caregivers. Here we show that a chronic treatment with trazodone, an antidepressant with positive effects on sleep, has beneficial effects on several cellular pathways contributing to neuroinflammation and tau pathology, in tauopathy-like rTg4510 mice. Trazodone also improved rapid eye movement (REM) sleep, the slowing of brain oscillations, and olfactory memory disturbances, which are all early symptoms observed in Alzheimer's disease. Thus, trazodone and compounds with REM sleep-promoting properties may represent a promising treatment approach to reduce the early symptoms of tauopathy and slow down disease progression.

Awareness and usage of personal protective equipment among construction workers and their hearing assessment by pure tone audiometry; A cross-sectional study in South India.

CONTEXT: Occupational hazards at the building construction workplace are enormous. Good compliance to the use of personal protective equipment (PPE) alongside other safety measures is critical in reducing the hazards. Occupational hearing loss is one such occupational hazard among construction workers. AIMS: This study was conducted for determining the awareness of the benefits of PPE and its usage among construction workers along with hearing assessment. SETTINGS AND DESIGN: A cross-sectional study was conducted among construction workers in a building project in Mangaluru city in Dakshina Kannada district in South India. METHODS AND MATERIAL: After obtaining informed written consent, 110 construction workers were recruited by simple random sampling method. Basic information, awareness, and use of PPE were collected using a semistructured questionnaire through personal interviews. Hearing assessment was done by pure tone audiometry. STATISTICAL ANALYSIS USED: Data collected was analyzed. Chi-square test and Fischer's exact probability test were used to test the association between variables. RESULTS: Majority (85.5%) of the construction workers are working for duration of 15 years or less. Difficulty in hearing was reported by 9.6% of them. Awareness regarding benefits of using personal protective devices like helmets, masks, and earplugs/muffs were 58.2%, 56.4%, and 14.5%, respectively. The utilization of at least one PPE among them was 58.1%. The presence of sensory neural hearing loss (SNHL) in both/either ears was detected among 14.5% of the workers. There was a statistically significant association of SNHL among workers and their duration of construction work. CONCLUSION: The current study highlights that awareness and usage of PPE was low and a proportion of them had a hearing impairment. Occupational health and safety training along with a periodic examination of construction workers need to be focused so as to detect and manage occupational health hazards early.

Quinazoline derivative from indigenous isolate, Nocardiopsis alba inhibits human telomerase enzyme.

AIM: Aim of this study was isolation and screening of various secondary metabolites produced by indigenous isolates of soil Actinomycetes for human telomerase inhibitory activity. METHODS AND RESULTS: Extracellular extract from culture suspension of various soil Actinomycetes species were tested for telomerase inhibitory activity. The organism which produced telomerase inhibitor was identified by 16S rRNA gene sequencing. The active fraction was purified by HPLC and analysed by GC-MS to identify the compound. In GC-MS analysis, the active principle was identified as 3-[4'-(2″-chlorophenyl)-2'-thiazolyl]-2,4-dioxo-1,2,3,4-tetrahydro quinazoline. The G-quadruplex stabilizing ability of the compound was checked by molecular docking and simulation experiments with G-quadruplex model (PDB ID-1L1H). The selective binding ability of the compound with G-quadruplex over Dickerson-Drew dodecamer DNA structures showed that the compound possess high selectivity towards G-quadruplex. CONCLUSIONS: Quinazoline derivative isolated from an indigenous strain of Nocardiopsis alba inhibited telomerase. Molecular docking and simulation studies predicted that this compound is a strong stabilizer of G-quadruplex conformation. It also showed a preferable binding to G-quadruplex DNA over normal DNA duplex. SIGNIFICANCE AND IMPACT OF THE STUDY: This particular compound can be suggested as a suitable compound for developing a future anticancer drug. The selectivity towards G-quadruplex over normal DNA duplex gives a clue that it is likely to show lower cytotoxicity in normal cells.