Sleep Analytics and Online Selective Anomaly Detection

We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science. Scientists have segmented sleep into several stages and stage two is characterized by two patterns (or anomalies) in the EEG time series recorded on sleep subjects. These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was introduced to design a residual system, where all anomalies (known and unknown) are detected but the system only triggers an alarm when non-SS anomalies appear. The solution of the OSAD problem required us to combine techniques from both machine learning and control theory. Experiments on data from real subjects attest to the effectiveness of our approach.Comment: Submitted to 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Dynamics of Sleep-Wake Transitions During Sleep

We study the dynamics of the awakening during the night for healthy subjects and find that the wake and the sleep periods exhibit completely different behavior: the durations of wake periods are characterized by a scale-free power-law distribution, while the durations of sleep periods have an exponential distribution with a characteristic time scale. We find that the characteristic time scale of sleep periods changes throughout the night. In contrast, there is no measurable variation in the power-law behavior for the durations of wake periods. We develop a stochastic model which agrees with the data and suggests that the difference in the dynamics of sleep and wake states arises from the constraints on the number of microstates in the sleep-wake system.Comment: Final form with some small corrections. To be published in Europhysics Letters, vol. 57, issue no. 5, 1 March 2002, pp. 625-63

Detecting K-complexes for sleep stage identification using nonsmooth optimisation

The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features

Analysis of the Temporal Organization of Sleep Spindles in the Human Sleep EEG Using a Phenomenological Modeling Approach

The sleep electroencephalogram (EEG) is characterized by typical oscillatory patterns such as sleep spindles and slow waves. Recently, we proposed a method to detect and analyze these patterns using linear autoregressive models for short (≈ 1 s) data segments. We analyzed the temporal organization of sleep spindles and discuss to what extent the observed interevent intervals correspond to properties of stationary stochastic processes and whether additional slow processes, such as slow oscillations, have to be assumed. We have found evidence for such an additional slow process, most pronounced in sleep stage 2

Impairments in Attention in Occasionally Snoring Children: An Event-Related Potential Study

Objective—To determine whether minimal snoring is benign in children. Procedure—22 rarely snoring children (mean age=6.9 years, 11 females) and age- and sexmatched controls participated in an auditory oddball task wearing 128-electrode nets. Parents completed Conner’s Parent Rating Scales-Revised Long (CPRS-R:L). Results—Snorers scored significantly higher on 4 CPRS-R:L subscales. Stepwise regression indicated that two ERP variables from a region of the ERP that peaked at 844 ms post-stimulus onset predicted CPRS-R:L ADHD Index scores. Conclusions—Occasional snorers according to parental report do exhibit ADHD-like behaviors. Basic sensory processing is longer than in controls, suggesting that delayed frontal activation requires more effort in snorers

Cross-translational studies in human and Drosophila identify markers of sleep loss

Inadequate sleep has become endemic, which imposes a substantial burden for public health and safety. At present, there are no objective tests to determine if an individual has gone without sleep for an extended period of time. Here we describe a novel approach that takes advantage of the evolutionary conservation of sleep to identify markers of sleep loss. To begin, we demonstrate that IL-6 is increased in rats following chronic total sleep deprivation and in humans following 30 h of waking. Discovery experiments were then conducted on saliva taken from sleep-deprived human subjects to identify candidate markers. Given the relationship between sleep and immunity, we used Human Inflammation Low Density Arrays to screen saliva for novel markers of sleep deprivation. Integrin αM (ITGAM) and Anaxin A3 (AnxA3) were significantly elevated following 30 h of sleep loss. To confirm these results, we used QPCR to evaluate ITGAM and AnxA3 in independent samples collected after 24 h of waking; both transcripts were increased. The behavior of these markers was then evaluated further using the power of Drosophila genetics as a cost-effective means to determine whether the marker is associated with vulnerability to sleep loss or other confounding factors (e.g., stress). Transcript profiling in flies indicated that the Drosophila homologues of ITGAM were not predictive of sleep loss. Thus, we examined transcript levels of additional members of the integrin family in flies. Only transcript levels of scab, the Drosophila homologue of Integrin α5 (ITGA5), were associated with vulnerability to extended waking. Since ITGA5 was not included on the Low Density Array, we returned to human samples and found that ITGA5 transcript levels were increased following sleep deprivation. These cross-translational data indicate that fly and human discovery experiments are mutually reinforcing and can be used interchangeably to identify candidate biomarkers of sleep loss

Cyclic and Sleep-Like Spontaneous Alternations of Brain State Under Urethane Anaesthesia

Background: Although the induction of behavioural unconsciousness during sleep and general anaesthesia has been shown to involve overlapping brain mechanisms, sleep involves cyclic fluctuations between different brain states known as active (paradoxical or rapid eye movement: REM) and quiet (slow-wave or non-REM: nREM) stages whereas commonly used general anaesthetics induce a unitary slow-wave brain state. Methodology/Principal Findings: Long-duration, multi-site forebrain field recordings were performed in urethaneanaesthetized rats. A spontaneous and rhythmic alternation of brain state between activated and deactivated electroencephalographic (EEG) patterns was observed. Individual states and their transitions resembled the REM/nREM cycle of natural sleep in their EEG components, evolution, and time frame (,11 minute period). Other physiological variables such as muscular tone, respiration rate, and cardiac frequency also covaried with forebrain state in a manner identical to sleep. The brain mechanisms of state alternations under urethane also closely overlapped those of natural sleep in their sensitivity to cholinergic pharmacological agents and dependence upon activity in the basal forebrain nuclei that are the major source of forebrain acetylcholine. Lastly, stimulation of brainstem regions thought to pace state alternations in sleep transiently disrupted state alternations under urethane. Conclusions/Significance: Our results suggest that urethane promotes a condition of behavioural unconsciousness tha

Increased deep sleep in a medication-free, detoxified female offender with schizophrenia, alcoholism and a history of attempted homicide: Case report

BACKGROUND: Psychiatric sleep research has attempted to identify diagnostically sensitive and specific sleep patterns associated with particular disorders. Both schizophrenia and alcoholism are typically characterized by a severe sleep disturbance associated with decreased amounts of slow wave sleep, the physiologically significant, refreshing part of the sleep. Antisocial behaviour with severe aggression, on the contrary, has been reported to associate with increased deep sleep reflecting either specific brain pathology or a delay in the normal development of sleep patterns. The authors are not aware of previous sleep studies in patients with both schizophrenia and antisocial personality disorder. CASE PRESENTATION: The aim of the present case-study was to characterize the sleep architecture of a violent, medication-free and detoxified female offender with schizophrenia, alcoholism and features of antisocial personality disorder using polysomnography. The controls consisted of three healthy, age-matched women with no history of physical violence. The offender's sleep architecture was otherwise very typical for patients with schizophrenia and/or alcoholism, but an extremely high amount of deep sleep was observed in her sleep recording. CONCLUSIONS: The finding strengthens the view that severe aggression is related to an abnormal sleep pattern with increased deep sleep. The authors were able to observe this phenomenon in an antisocially behaving, violent female offender with schizophrenia and alcohol dependence, the latter disorders previously reported to be associated with low levels of slow wave sleep. New studies are, however, needed to confirm and explain this preliminary finding

Amplitude Reduction and Phase Shifts of Melatonin, Cortisol and Other Circadian Rhythms after a Gradual Advance of Sleep and Light Exposure in Humans

Background: The phase and amplitude of rhythms in physiology and behavior are generated by circadian oscillators and entrained to the 24-h day by exposure to the light-dark cycle and feedback from the sleep-wake cycle. The extent to which the phase and amplitude of multiple rhythms are similarly affected during altered timing of light exposure and the sleepwake cycle has not been fully characterized. Methodology/Principal Findings: We assessed the phase and amplitude of the rhythms of melatonin, core body temperature, cortisol, alertness, performance and sleep after a perturbation of entrainment by a gradual advance of the sleep-wake schedule (10 h in 5 days) and associated light-dark cycle in 14 healthy men. The light-dark cycle consisted either of moderate intensity ‘room ’ light (,90–150 lux) or moderate light supplemented with bright light (,10,000 lux) for 5 to 8 hours following sleep. After the advance of the sleep-wake schedule in moderate light, no significant advance of the melatonin rhythm was observed whereas, after bright light supplementation the phase advance was 8.1 h (SEM 0.7 h). Individual differences in phase shifts correlated across variables. The amplitude of the melatonin rhythm assessed under constant conditions was reduced after moderate light by 54 % (17–94%) and after bright light by 52 % (range 12–84%), as compared to the amplitude at baseline in the presence of a sleep-wake cycle. Individual differences in amplitude reduction of the melatonin rhythm correlated with the amplitude of body temperature, cortisol and alertness

Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea

Cardiac and respiratory rhythms reveal transient phases of phase-locking which were proposed to be an important aspect of cardiorespiratory interaction. The aim of this study was to quantify cardio-respiratory phase-locking in obstructive sleep apnea (OSA). We investigated overnight polysomnography data of 248 subjects with suspected OSA. Cardiorespiratory phase-coupling was computed from the R-R intervals of body surface ECG and respiratory rate, calculated from abdominal and thoracic sensors, using Hilbert transform. A significant reduction in phase-coupling was observed in patients with severe OSA compared to patients with no or mild OSA. Cardiorespiratory phase-coupling was also associated with sleep stages and was significantly reduced during rapid-eye-movement (REM) sleep compared to slow-wave (SW) sleep. There was, however, no effect of age and BMI on phase coupling. Our study suggests that the assessment of cardiorespiratory phase coupling may be used as an ECG based screening tool for determining the severity of OSA