Current clinical standards to assess sleep and its disorders lack either accuracy or user-friendliness. They are therefore difficult to use in cost-effective population-wide screening or long-term objective follow-up after diagnosis. In order to fill this gap, the use of cardiac and respiratory information was evaluated for discrimination between different sleep stages, and for detection of apneic breathing. Alternative probabilistic visual representations were also presented, referred to as the hypnocorrogram and apneacorrogram. Analysis was performed on the UCD sleep apnea database, available on Physionet. The presence of apneic events proved to have a significant impact on the performance of a cardiac and respiratory based algorithm for sleep stage classification. WAKE versus SLEEP discrimination resulted in a kappa value of κ=0.439 ?>, while REM versus NREM resulted in κ=0.298 ?> and light sleep (N1N2) versus deep sleep (N3) in κ=0.339 ?>. The high proportion of hypopneic events led to poor detection of apneic breathing, resulting in a kappa value of κ=0.272 ?>. While the probabilistic representations allow to put classifier output in perspective, further improvements would be necessary to make the classifier reliable for use on patients with sleep apnea.

Over the past years, ambient intelligence has infiltrated our lives through various home applications, enabled by the decreasing size and cost of computing technology. While in transport or industry, its presence has become second nature; some areas, such as our bedroom, have remained fairly untouched. Since our bedroom hosts the beginning and end of our daily activities, it needs to assist us in the recovery and preparation of daily activities. Therefore, it holds an enormous opportunity for AI applications, which do exactly what is needed: sensibly assist the user, learn his preferences, and react to his/her mood and needs. This chapter outlines the different ways of assisting the user in his/her intelligent bedroom: ways to monitor health, improve both physical and mental recovery during the night by automatically optimising the environment, as well as automate a number of tedious tasks that reoccur at every start and end of the day.

Misperception of Sleep Onset Latency, often found in Primary Insomnia, has been cited to be influenced by hyperarousal, reflected in EEG- and ECG-related indices. The aim of this retrospective study was to examine the association between Central Nervous System (i.e. EEG) and Autonomic Nervous System activity in the Sleep Onset Period and the first NREM sleep cycle in Primary Insomnia (n = 17) and healthy controls (n = 11). Furthermore, the study examined the influence of elevated EEG and Autonomic Nervous System activity on Stage2 sleep-protective mechanisms (K-complexes and sleep spindles). Confirming previous findings, the Primary Insomnia-group overestimated Sleep Onset Latency and this overestimation was correlated with elevated EEG activity. A higher amount of beta EEG activity during the Sleep Onset Period was correlated with the appearance of K-complexes immediately followed by a sleep spindle in the Primary Insomnia-group. This can be interpreted as an extra attempt to protect sleep continuity or as a failure of the sleep-protective role of the K-complex by fast EEG frequencies following within one second. The strong association found between K-alpha (K-complex within one second followed by 8–12 Hz EEG activity) in Stage2 sleep and a lower parasympathetic Autonomic Nervous System dominance (less high frequency HR) in Slow-wave sleep, further assumes a state of hyperarousal continuing through sleep in Primary Insomnia.

The aim of this cross-sectional study was to investigate the influence of media use in the hour before bedtime on sleep/wake patterns and daytime functioning among adolescents and to examine the moderating role of parental control. A total of 1,926 Belgian students, 55% girls and 45% boys, with a mean age of 16.9 1.5 years, completed a modified version of the School Sleep Habits Survey. Correlational analyses showed that media use, except television viewing, was associated with later bedtimes and longer sleep latencies. Cell phone and computer usage was negatively associated with daytime functioning. On schooldays, parental control had a moderating effect on the relationship between bedtime and computer use (beta =.05; p <.05) and between bedtime and mp3 player use (beta =.08; p <.01). During the weekend, parental control played a moderating role between bedtime and television viewing (beta =.06; p =.01). As media use can influence the sleep of adolescents considerably, parental control is necessary to regulate the exposure of adolescents to media and to moderate the detrimental effect of media use on sleep.

Off-body detection of respiratory and cardiac activity presents an enormous opportunity for general health, stress and sleep quality monitoring. The presented setup detects the mechanical activity of both heart and lungs by measuring pressure difference fluctuations between two air volumes underneath the chest area of the subject. The registered signals were characterized over four different sleep postures, three different base air pressures within the air volumes and three different mattress top layer materials. Highest signal strength was detected in prone posture for both the respiratory and heart beat signal. Respiratory signal strength was the lowest in supine posture, while heart beat signal strength was lowest for right lateral. Heart beat cycle variability was highest in prone and lowest in supine posture. Increasing the base air pressure caused a reduction in signal amplitude for both the respiratory and the heart beat signal. A visco-elastic poly-urethane foam top layer had significantly higher respiration amplitude compared to high resilient poly-urethane foam and latex foam. For the heart beat signal, differences between the top layers were small. The authors conclude that, while the influence of the mattress top layer material is small, the base air pressure can be tuned for optimal mechanical transmission from heart and lungs towards the registration setup.

Automated analysis of sleep apnea based on single-lead electrocardiogram would make screening and diagnosis much more accessible. Over the years, several algorithms have been proposed in the literature. In most of them, one or several temporal averaging techniques are used to improve classifier performance. A comprehensive comparison between those techniques however has never been published. Four different temporal averaging techniques, as well as overlapping of segments, were independently assessed using a database of 70 night-time recordings, originally released for the Computers in Cardiology challenge in 2000. Classification was performed with an LDA classifier. Multiple problem-specific feature sets of 10 features were selected out of a complete set of 304 using a two-step approach. Averaging classifier input features over neighboring segments led to the highest agreement values on the test set, outperforming the best automatic entry during the original competition (90.4% vs 89.4%). When combining classifier output values, an odd amount of segments should be used. Calculating features on larger segments (> i-min) led to the worst results, possibly explained by its higher susceptibility to noise. Overlapping of segments improved overall agreement by about 1%.

Polysomnography (PSG) is considered the gold standard to assess sleep accurately, but it can be expensive, time-consuming, and uncomfortable, specifically in long-term sleep studies. Actigraphy, on the other hand, is both cheap and user-friendly, but depending on the application lacks detail and accuracy. Our aim was to evaluate cardiorespiratory and movement signals in discriminating between wake, rapid-eye-movement (REM), light (N1N2), and deep (N3) sleep. The dataset comprised 85 nights of PSG from a healthy population. Starting from a total of 750 characteristic variables (features), problem-specific subsets of 40 features were forwardly selected using the combination of a wrapper method (Cohen's kappa statistic on radial basis function (RBF)-kernel support vector machine (SVM) classifier) and filter method (minimum redundancy maximum relevance criterion on mutual information). Final classification was performed using an RBF-kernel SVM. Non-subject-specific wake versus sleep classification resulted in a Cohen's kappa value of 0.695, while REM versus NREM resulted in 0.558 and N3 versus N1N2 in 0.553. The broad pool of initial features gave insight in which features discriminated best between the different classes. The classification results demonstrate the possibility of making long-term sleep monitoring more widely available.

Deep tissue injury (DTI) is a localized area of tissue necrosis that originates in the subcutaneous layers under an intact skin and tends to develop when soft tissue is compressed for a prolonged period of time. In clinical practice, DTI is particularly common in bedridden patients and remains a serious issue in todays health care. Repositioning is generally considered to be an effective preventive measure of pressure ulcers. However, limited experimental research and no computational studies have been undertaken on this method. In this study, a methodology was developed to evaluate the influence of different repositioning intervals on the location, size and severity of DTI in bedridden patients. The spatiotemporal evolution of compressive stresses and skeletal muscle viability during the first 48 h of DTI onset was simulated for repositioning schemes in which a patient is turned every 2, 3, 4 or 6 h. The model was able to reproduce important experimental findings, including the morphology and location of DTI in human patients as well as the discrepancy between the internal tissue loads and the contact pressure at the interface with the environment. In addition, the model indicated that the severity and size of DTI were reduced by shortening the repositioning intervals. In conclusion, the computational framework presented in this study provides a promising modelling approach that can help to objectively select the appropriate repositioning scheme that is effective and efficient in the prevention of DTI.

This study implements an algorithm for the autonomous control of spinal alignment during sleep by the active adjustment of mechanical bed characteristics according to the adopted sleep posture. Bed systems were used that allow active control of the mechanical stiffness in eight comfort zones by means of separately adjustable actuators. Mattress indentation measurements provide the input to detect body movement, recognize sleep posture, and --by combination with a subject specific human model --estimate spine shape. Comparison between the estimated spine shape and the desired shape results in new target values for the actuators. The control loop is repeated until the desired spine shape is reached. Results of overnight experiments revealed a significant improvement of spinal alignment during nights with active control of bed properties compared to a reference night without control. In addition, a significant improvement on subjectively perceived sleep quality was demonstrated after sleeping on the actively controlled systems.

Ergonomic sleep studies benefit from long-term monitoring in the home environment to cope with daily variations and habituation effects. Polysomnography allows to asses sleep accurately, but is costly, time-consuming and possibly disturbing for the sleeper. Actigraphy is cheap and user friendly, but for many studies lacks accuracy and detailed information. This proof-of-concept study investigates Least-Squares Support Vector Machines as a tool for automatic sleep stage classification (Wake-N1-Rem to N2-N3 separation), using automatic trainingset-specific filtered features as derived from three easy to register signals, namely heart rate, breathing rate and movement. The algorithms are trained and validated using 20 nights out of a 600 night database from over 100 different healthy persons. Different training and test set strategies were analyzed leading to different results. The more person-specific the training nights to the test nights, the better the classification accuracy as validated against the hypnograms scored by experts from the full polysomnograms. In the limit of complete person-specific training, the accuracy of the algorithm on the test set reached 94%. This means that this algorithm could serve its use in long-term monitoring sleep studies in the home environment, especially when prior person-specific polysomnographic training is performed.

The sleep system (i.e. the combination of mattress and bed base) is an important factor of the sleep environment since it allows physical recuperation during sleep by providing proper body support. However, various factors influence the interaction between the human body and the sleep system. Contributing factors include body dimensions, distribution of body weight and stiffness of the sleep system across the mattress surface. During the past decade, the rise of several new bedding technologies has made it increasingly difficult for the consumer to select a proper sleep system. Therefore, this study presents a method to model human-bed interaction in order to objectively predict the ideal sleep system for a particular individual. The proposed method combines a personalized anthropometric model with standardized load-deflection characteristics of mattress and bed base. Results for lateral sleep positions show a root mean square deviation of 11.9 ± 6.1 mm between modeled spine shapes and validation shapes, derived from 3D surface scans of the back surface. The method showed to be a reliable tool to individually identify the sleep system providing superior support from a variety of possible mattress-bed base combinations.

Insomnia Disorder (ID) is the second-most common mental disorder and has a far-reaching impact on daytime functioning. A meta-analysis indicates that, of all cognitive domains, declarative memory involving the hippocampus is most affected in insomnia. Hippocampal functioning has consistently been shown to be sensitive to experimental sleep deprivation. Insomnia however differs from sleep deprivation in many aspects, and findings on hippocampal structure and function have been equivocal. The present study used both structural and resting-state functional Magnetic Resonance Imaging in a larger sample than previously reported to evaluate hippocampal volume and functional connectivity in ID. Included were 65 ID patients (mean age = 48.3 y ± 14.0, 17 males) and 65 good sleepers (mean age = 44.1 y ± 15.2, 23 males). Insomnia severity was assessed with the Insomnia Severity Index (ISI), subjective sleep with the Consensus Sleep Diary (CSD) and objective sleep by two nights of polysomnography (PSG). Seed-based analysis showed a significantly stronger connectivity of the bilateral hippocampus with the left middle frontal gyrus in ID than in controls (p = .035, cluster based correction for multiple comparisons). Further analyses across all participants moreover showed that individual differences in the strength of this connectivity were associated with insomnia severity (ISI, r = 0.371, p = 9.3e−5) and with subjective sleep quality (CSD sleep efficiency, r = −0.307, p = .009) (all p FDR-corrected). Hippocampal volume did not differ between ID and controls. The findings indicate more severe insomnia and worse sleep quality in people with a stronger functional connectivity between the bilateral hippocampus and the left middle frontal gyrus, part of a circuit that characteristically activates with maladaptive rumination and deactivates with sleep.

Despite a long history of interest in emotion regulation as well as in the mechanisms that regulate sleep, the relationship between emotion regulation and sleep is not yet well understood. The present study investigated whether "an experiential approach"-defined by coping through affectively acknowledging, understanding, and expressing actual emotional experience and affective feeling about a situation-compared with a "cognitive analytical approach"-defined by the cognitive analysis of the causes, meanings and implications of the situation for the own self-would buffer the impact of an emotional failure experience on (1) emotional experience and (2) sleep structure assessed by EEG polysomnography. Twenty-eight healthy volunteers participated in this study. A direct comparison of the two emotion regulation strategies revealed that participants who were instructed to apply an experiential approach showed less fragmentation of sleep than participants who were instructed to apply an analytical approach. The use of an experiential approach resulted in a longer sleep time, higher sleep efficiency, fewer awakenings, less % time awake, and fewer minutes wake after sleep onset. Implications of the differential effects of these two forms of emotion regulation on sleep are discussed.

Proper body support plays an import role in the recuperation of our body during sleep. Therefore, this study uses an automatically adapting bedding system that optimises spinal alignment throughout the night by altering the stiffness of eight comfort zones. The aim is to investigate the influence of such a dynamic sleep environment on objective and subjective sleep parameters. The bedding system contains 165 sensors that measure mattress indentation. It also includes eight actuators that control the comfort zones. Based on the measured mattress indentation, body movements and posture changes are detected. Control of spinal alignment is established by fitting personalized human models in the measured indentation. A total of 11 normal sleepers participated in this study. Sleep experiments were performed in a sleep laboratory where subjects slept three nights: a first night for adaptation, a reference night and an active support night ( in counterbalanced order). Polysomnographic measurements were recorded during the nights, combined with questionnaires aiming at assessing subjective information. Subjective information on sleep quality, daytime quality and perceived number of awakenings shows significant improvements during the active support (ACS) night. Objective results showed a trend towards increased slow wave sleep. On the other hand, it was noticed that % N1-sleep was significantly increased during ACS night, while % N2-sleep was significantly decreased. No prolonged N1 periods were found during or immediately after steering.