Search

Purpose: There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring. Methods: We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders. Results: The classifier achieved substantial agreement on four-class sleep staging with an average Cohen’s kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa (ρ = −0.30, p<0.001) and age and accuracy (ρ = −0.22, p<0.001). Conclusion: This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis

Unobtrusive sleep monitoring in children presents challenges. We investigated the possibility of using an ultra-wide band (UWB) radar to measure sleep in children. Tested on 32 children undergoing a clinical polysomnography, the use of 38 features extracted from radar signals and an adaptive boosting algorithm led to promising results in estimating sleep stages.

Narcolepsy is a chronic sleep disorder with a strong negative impact on quality of life, especially when untreated. Diagnostic delay is a persistent problem, with obvious detrimental effects on patients. A diagnosis of narcolepsy may be delayed because of its broad symptom presentation which is much more encompassing than the classical "tetrad" of sleepiness, cataplexy, hallucinations, and sleep paralysis. Furthermore, symptoms can vary over time. Presentation of symptoms can also be markedly different between children and adults. Finally, common sleep-related comorbidities increase the risk of narcolepsy being masked by overlapping symptoms or treatment. In this review, we provide a detailed description of the broad and dynamic symptom spectrum of narcolepsy, with specific attention to the different manifestations in both adults and children. The overarching goal is to help not only sleep specialists, but general practitioners, pediatricians, and other caregivers with early recognition and prompt diagnosis of this severe but treatable disorder.

Rest–activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long‐term monitoring of rest–activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest–activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self‐reports within healthy participants. First‐year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user‐reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest–activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long‐term monitoring of (disturbances to) rest–activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep‐related problems, or in populations for whom disturbances in rest–activity patterns are secondary complaints, such as neurological disorders.

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online., Circuits and Systems

Study Objectives: Narcolepsy with cataplexy is tightly associated with the HLA class II allele DQB1*06:02. Evidence indicates a complex contribution of HLA class II genes to narcolepsy susceptibility with a recent independent association with HLA-DPB1. The cause of narcolepsy is supposed be an autoimmune attack against hypocretin-producing neurons. Despite the strong association with HLA class II, there is no evidence for CD4+ T-cell-mediated mechanism in narcolepsy. Since neurons express class I and not class II molecules, the final effector immune cells involved might include class I-restricted CD8+ T-cells. Methods: HLA class I (A, B, and C) and II (DQB1) genotypes were analyzed in 944 European narcolepsy with cataplexy patients and in 4,043 control subjects matched by country of origin. All patients and controls were DQB1*06:02 positive and class I associations were conditioned on DQB1 alleles. Results: HLA-A*11:01 (OR = 1.49 [1.18-1.87] P = 7.0*10−4), C*04:01 (OR = 1.34 [1.10-1.63] P = 3.23*10−3), and B*35:01 (OR = 1.46 [1.13-1.89] P = 3.64*10−3) were associated with susceptibility to narcolepsy. Analysis of polymorphic class I amino-acids revealed even stronger associations with key antigen-binding residues HLA-A-Tyr9 (OR = 1.32 [1.15-1.52] P = 6.95*10−5) and HLA-C-Ser11 (OR = 1.34 [1.15-1.57] P = 2.43*10−4). Conclusions: Our findings provide a genetic basis for increased susceptibility to infectious factors or an immune cytotoxic mechanism in narcolepsy, potentially targeting hypocretin neurons. Significance Although evidence strongly indicates that immune-related mechanisms are involved in the pathogenesis of narcolepsy with hypocretin deficiency, functional data are missing. The strong association with HLA class II genes is not corroborated by any CD4+ T Cell implication and inflammation. Additionally, these genes are not normally expressed in the brain. Here, we show that several HLA class I variants are associated with narcolepsy. Since these mol

Lieke WA Hermans,1 Marta Regis,2 Pedro Fonseca,1,3 Bertram Hoondert,4 Tim RM Leufkens,3 Sebastiaan Overeem,1,4 Merel M van Gilst1,4 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; 2Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands; 3Philips Research, Eindhoven, the Netherlands; 4Sleep Medicine Center Kempenhaeghe, Heeze, the NetherlandsCorrespondence: Lieke WA HermansDepartment of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600, MB Eindhoven, the NetherlandsEmail l.w.a.hermans@tue.nlIntroduction: Assessing objective measures of sleep fragmentation could yield important features reflecting impaired sleep quality in people with insomnia. Survival analysis allows the specific examination of the stability of NREM sleep, REM sleep and wake. The objective of this study was to assess the differences between survival dynamics of NREM sleep, REM sleep and wake between people with insomnia and healthy controls.Methods: We analyzed retrospective polysomnography recordings from 86 people with insomnia and 94 healthy controls. For each participant, survival dynamics of REM sleep, NREM sleep and wake were represented using Weibull distributions. We used lasso penalized parameter selection in combination with linear regression to analyze the difference between participant groups with respect to the Weibull scale and shape parameters, while correcting for age, sex, total sleep time and relevant interaction effects.Results: Significant effects of group were found for the NREM scale parameter, and for the wake scale and shape parameters. Results indicated that people with insomnia had less stable NREM sleep and more stable wake after sleep onset compared to healthy controls. Additionally, the altered distribution of wake segment lengths indicated an increased difficulty to fall asleep after longer awakenings in the insomnia group. However, the

Bernice M Wulterkens,1,2,&ast; Pedro Fonseca,1,2,&ast; Lieke WA Hermans,1 Marco Ross,3 Andreas Cerny,3 Peter Anderer,3 Xi Long,1,2 Johannes P van Dijk,1,4 Nele Vandenbussche,4 Sigrid Pillen,1,4 Merel M van Gilst,1,4 Sebastiaan Overeem1,4 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; 2Philips Research, Eindhoven, the Netherlands; 3Sleep and Respiratory Care, Home Healthcare Solutions, Philips Austria GmbH, Vienna, Austria; 4Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands&ast;These authors contributed equally to this workCorrespondence: Bernice M WulterkensEindhoven University of Technology, Department of Electrical Engineering, Room Flux 7.104, PO BOX 513, Eindhoven, 5600 MB, the NetherlandsEmail b.m.wulterkens@tue.nlPurpose: There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.Methods: We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.Results: The classifier a

Objective: Respiratory activity is an essential parameter to monitor healthy and disordered sleep, and unobtrusive measurement methods have important clinical applications in diagnostics of sleep-related breathing disorders. We propose a respiratory activity surrogate extracted from wrist-worn reflective photoplethysmography validated on a heterogeneous dataset of 389 sleep recordings. Approach: The surrogate was extracted by interpolating the amplitude of the PPG pulses after evaluation of pulse morphological quality. Subsequent multistep post-processing was applied to remove parts of the surrogate with low quality and high motion levels. In addition to standard respiration rate performance, we evaluated the similarity between surrogate respiratory activity and reference inductance plethysmography of the thorax, using Spearman's correlations and spectral coherence, and assessed the influence of PPG signal quality, motion levels, sleep stages and obstructive sleep apnea. Main results: Prior to post-processing, the surrogate already had a strong similarity with the reference (correlation=0.54, coherence=0.81), and reached respiration rate estimation performance in line with the literature (estimation error=0.76±2.11 breaths/min). Detrimental effects of low PPG quality, high motion levels and sleep stage-dependent physiological phenomena were significantly mitigated by the proposed post-processing steps (correlation=0.62, coherence=0.88, estimation error=0.53±1.98 breaths/min). Significance: Wrist-worn PPG can be used to extract respiratory activity, thus allowing respiration monitoring in real-world sleep medicine applications using (consumer) wearable devices.

Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders. To study underlying mechanisms for insomnia, we studied autonomic activity changes around sleep onset in participants without clinical insomnia but with varying problems with initiating or maintaining sleep quantified as increased sleep onset latency (SOL) and wake after sleep onset (WASO), respectively. Polysomnography and electrocardiography were simultaneously recorded in 176 participants during a single night. Cardiac autonomic activity was assessed using frequency domain analysis of RR intervals and results show that the normalized spectral power in the low frequency band (LF nu) after sleep onset was significantly higher in participants with long SOL compared to participants with short SOL. Furthermore, the normalized spectral power in the high frequency band (HF nu) was significantly lower in participants with long SOL as compared to participants with short SOL over 3 time periods (first 10 min in bed intending to sleep, 10 min before, and 10 min after sleep onset). These results suggest that participants with long SOL are more aroused in all three examined time periods when compared to participants with short SOL, especially for young adults (20–40 years). As there is no clear consensus on the cutoff for an increased WASO, we used a data-driven approach to explore different cutoffs to define short WASO and long WASO groups. LF nu, HF nu, and LF/HF differed between the long and the short WASO groups. A higher LF nu and LF/HF and a lower HF nu was observed in participants with long WASO for most cutoffs. The highest effect size was found using the cutoff of 66 min. Our findings suggest that autonomic cardiac activity has predictive value with respect to sleep characteristics pertaining to sleep onset and maintenance.

We aimed to compare HLA-DQB1-associations in narcolepsy type 1 (NT1) patients with disease onset before and after the 2009 H1N1 pandemic in a large Dutch cohort. 525 NT1 patients and 1272 HLA-DQB1*06:02-positive healthy controls were included. Because of the discussion that has arisen on the existence of sporadic and post-H1N1 NT1, HLA-DQB1-associations in pre- and post-H1N1 NT1 patients were compared. The associations between HLA-DQB1 alleles and NT1 were not significantly different between pre- and post-H1N1 NT1 patients. Both HLA-DQB1-associations with pre- and -post H1N1 NT1 reported in recent smaller studies were replicated. Our findings combine the results of studies in pre- and post-H1N1 NT1 and argue against considering post-H1N1 NT1 as a different entity.

We describe the process and results of a study into the needs of a sleep diary for children that are suffering of insomnia. Applying a methodology oriented on the user we have identified needs of patients and professionals, transcribed them into functional and non-functional requirements, and created a low fidelity prototype.

Problems initiating or maintaining sleep are prevalent and impact the quality of life negatively. Negative thinking patterns may perpetuate insomnia by inducing a state of arousal and consequently disrupting sleep. 'Thought challenging' is a common strategy to adopt a positive and peaceful mindset, but requires high awareness to internalize rational reasoning. Regular self-report and feedback may support the acquisition of fundamental reflection skills. We developed a thought journal in a mobile app to facilitate thought challenging. With the app, the users can reflect on daily situations and get visualized summaries as feedback. We carried out one week trial to explore perceived benefit, motivation, user engagement, and its integration with a sleep support tool. The results showed that using the app improved self-reflection skills and visualized summaries are perceived as motivating to log thoughts.

BACKGROUND: Narcolepsy is a chronic sleep disorder with a broad variety of symptoms. Although narcolepsy is primarily characterized by excessive daytime sleepiness and cataplexy (loss of muscle control triggered by emotions), patients may suffer from hypnagogic hallucinations, sleep paralysis, and fragmented night sleep. However, the spectrum of narcolepsy also includes symptoms not related to sleep, such as cognitive or psychiatric problems. Symptoms vary greatly among patients and day-to-day variance can be considerable. Available narcolepsy questionnaires do not cover the whole symptom spectrum and may not capture symptom variability. Therefore, there is a clinical need for tools to monitor narcolepsy symptoms over time to evaluate their burden and the effect of treatment.OBJECTIVE: This study aimed to describe the design, development, implementation, and evaluation of the Narcolepsy Monitor, a companion app for long-term symptom monitoring in narcolepsy patients.METHODS: After several iterations during which content, interaction design, data management, and security were critically evaluated, a complete version of the app was built. The Narcolepsy Monitor allows patients to report a broad spectrum of experienced symptoms and rate their severity based on the level of burden that each symptom imposes. The app emphasizes the reporting of changes in relative severity of the symptoms. A total of 7 patients with narcolepsy were recruited and asked to use the app for 30 days. Evaluation was done by using in-depth interviews and user experience questionnaire.RESULTS: We designed and developed a final version of the Narcolepsy Monitor after which user evaluation took place. Patients used the app on an average of 45.3 (SD 19.2) days. The app was opened on 35% of those days. Daytime sleepiness was the most dynamic symptom, with a mean number of changes of 5.5 (SD 3.7) per month, in contrast to feelings of anxiety or panic, which was only moved 0.3 (