Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data

In this paper we propose a new machine learning model for classification of nocturnal awakenings in acute insomnia and normal sleep. The model does not require sleep diaries or any other subjective information from the individuals who took part of the study. It is based on nocturnal actigraphy collected from pre-medicated individuals with acute insomnia and normal sleep controls. We have derived dynamical and statistical features from the actigraphy time series data. These features are combined using two machine learning techniques namely Random Forest (RF) and Support Vector Machine (SVM). RF shows better performance (accuracy-84%) than SVM (73%) in classifying individuals with insomnia from healthy sleepers. The developed model provides a signature of the condition of acute insomnia obtained from actigraphy only and is very promising as a tool to detect the condition in a non-invasive way and without sleep diaries or any other subjective information

Differentiating acute from chronic insomnia with machine learning from actigraphy time series data

Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Wearable Sleep Technology in Clinical and Research Settings

The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine

Sleep characteristics and mood of professional esports athletes: A multi-national study

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Esports is becoming increasingly professionalized, yet research on performance management is remarkably lacking. The present study aimed to investigate the sleep and mood of professional esports athletes. Participants were 17 professional esports athletes from South Korea (N = 8), Australia (N = 4), and the United States (N = 5) who played first person shooter games (mean age 20 ± 3.5 years, 100% male). All participants wore a wrist-activity monitor for 7–14 days and completed subjective sleep and mood questionnaires. Participants had a median total sleep time of 6.8 h and a sleep efficiency of 86.4% per night. All participants had significantly delayed sleep patterns (median sleep onset 3:43 a.m. and wake time 11:24 a.m.). Participants had a median sleep onset latency of 20.4 min and prolonged wake after sleep onset of 47.9 min. Korean players had significantly higher depression scores compared to the other groups (p \u3c 0.01) and trained longer per day than the Australian or United States teams (13.4 vs. 4.8 vs. 6.1 h, respectively). Depression scores were strongly correlated with number of awakenings, wake after sleep onset, and daily training time (p \u3c 0.05). As the first pilot sleep study in the esports field, this study indicates that esports athletes show delayed sleep patterns and have prolonged wake after sleep onset. These sleep patterns may be associated with mood (depression) and training time. Sleep interventions designed specifically for esports athletes appear warranted

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Sleep homeostasis in the European jackdaw (<i>Coloeus monedula</i>):Sleep deprivation increases NREM sleep time and EEG power while reducing hemispheric asymmetry

Introduction: Sleep is a wide-spread phenomenon that is thought to occur in all animals. Yet, the function of it remains an enigma. Conducting sleep experiments in different species may shed light on the evolution and functions of sleep. Therefore, we studied sleep architecture and sleep homeostatic responses to sleep deprivation in the European jackdaw (Coloeus monedula).Methods: A total of nine young adult birds were implanted with epidural electrodes and equipped with miniature data loggers for recording movement activity (accelerometery) and electroencephalogram (EEG). Individually-housed jackdaws were recorded under controlled conditions with a 12:12-h light-dark cycle.Results: During baseline, the birds spent on average 48.5% of the time asleep (39.8% non-rapid eye movement (NREM) sleep and 8.7% rapid eye movement (REM) sleep). Most of the sleep occurred during the dark phase (dark phase: 75.3% NREM sleep and 17.2% REM sleep; light phase 4.3% NREM sleep and 0.1% REM sleep). After sleep deprivation of 4 and 8 h starting at lights off, the birds showed a dose-dependent increase in NREM sleep time. Also, NREM sleep EEG power in the 1.5–3 Hz frequency range, which is considered to be a marker of sleep homeostasis in mammals, was significantly increased for 1-2 h after both 4SD and 8SD. While there was little true unihemispheric sleep in the Jackdaws, there was a certain degree of hemispheric asymmetry in NREM sleep EEG power during baseline, which reduced after sleep deprivation in a dose-dependent manner.Conclusion: In conclusion, jackdaws display homeostatic regulation of NREM sleep and sleep pressure promotes coherence in EEG power

Disease state prediction using multiscale dynamics

Treatment of many disorders requires clinic visits, with a strong reliance on patient self-reports for filling in the gaps between these in-person assessments. This lack of monitoring leads to biases in the data and a lack of provision for responding to rapid changes in health. Objective tools for monitoring patients in between clinical visits are mainly absent. In recent years, advances in wearable technology have enabled almost real-time monitoring of changes in physiology. These advances have the potential to transform the landscape for monitoring diseases. The objective of this research is to build non-invasive and continuous monitoring methods using wearables in naturalistic settings. In the first part, a novel wearable-based sleep detection approach is developed. The proposed approach uses the variations observed in the physiological data and detects patterns in these change events associated with sleep-wake transitions. The second part focuses on features derived from wearables to separate healthy controls and participants with health disorders in two different applications. In the first application, research watch data is used for monitoring patient post-trauma. Lastly, the second application presents the use of both passive (motion, location, social contact) and active (clinically validated survey) data collected by a smartphone app for heart failure outcome estimation.Ph.D