Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use

A knowledge model for the development of a framework for hypnogram construction

The final publication is available via http://dx.doi.org/10.1016/j.knosys.2016.11.016[Abstract] We describe a proposal of a knowledge model for the development of a framework for hypnogram construction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twentieth century, after the development of electroencephalography (EEG) by Hans Berger, there have been multiple studies on human sleep and its structure. Polysomnography (PSG), a sleep study from several biophysiological variables, gives us the hypnogram, a graphic representation of the stages of sleep as a function of time. This graph, when analyzed in conjunction with other physiological parameters, such as the heart rate or the amount of oxygen in arterial blood, has become a valuable diagnostic tool for different clinical problems that can occur during sleep and that often cause poor quality sleep. Currently, the gold standard for the detection of sleep events and for the correct classification of sleep stages are the rules published by the American Academy of Sleep Medicine (AASM), version 2.2. Based on the standards available to date, different studies on methods of automatic analysis of sleep and its stages have been developed but because of the different development and validation procedures used in existing methods, a rigorous and useful comparative analysis of results and their ability to correctly classify sleep stages is not possible. In this sense, we propose an approach that ensures that sleep stage classification task is not affected by the method for extracting PSG features and events. This approach is based on the development of a knowledge-intensive base system (KBS) for classifying sleep stages and building the corresponding hypnogram. For this development we used the CommonKADS methodology, that has become a de facto standard for the development of KBSs. As a result, we present a new knowledge model that can be used for the subsequent development of an intelligent system for hypnogram construction that allows us to isolate the process of signal processing to identify sleep stages so that the hypnograms obtained become comparable, independently of the signal analysis techniques.Xunta de Galicia; GRC2014/035Ministerio de Economía y Competitividad; TIN2013-40686-

Technique of automated hypnogram construction

The technique of automated sleep stage recognition and hypnogram construction has been considered. For partition of initial polysomnogram by segments obtained as a result of patient sleep monitoring the signal energy is analyzed using nonlinear energy controller. Frequency weighted energy is calculated for all registered signals then averaging and segmentation occur according to monitored signals behavior. Secondary index vector which is used at transition from segments to fixed duration periods is formed for segments. One or another sleep stage is finally assigned to the period by correlation analysis. Accuracy of the developed algorithm is connected with quantity of considered secondary indices, maximally detailed description of sleep stage characteristics and realization of training by manually prepared example

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. This paper is a revamping of Modeling multilevel sleep transitional data via Poisson log-linear multilevel models available at: http://www.bepress.com/jhubiostat/paper212

Monitoring sleep depth: analysis of bispectral index (BIS) based on polysomnographic recordings and sleep deprivation

© 2015 Springer Science+Business Media Dordrecht The assessment and management of sleep are increasingly recommended in the clinical practice. Polysomnography (PSG) is considered the gold standard test to monitor sleep objectively, but some practical and technical constraints exist due to environmental and patient considerations. Bispectral index (BIS) monitoring is commonly used in clinical practice for guiding anesthetic administration and provides an index based on relationships between EEG components. Due to similarities in EEG synchronization between anesthesia and sleep, several studies have assessed BIS as a sleep monitor with contradictory results. The aim of this study was to evaluate objectively both the feasibility and reliability of BIS for sleep monitoring through a robust methodology, which included full PSG recordings at a baseline situation and after 40 h of sleep deprivation. Results confirmed that the BIS index was highly correlated with the hypnogram (0.89 ± 0.02), showing a progressive decrease as sleep deepened, and an increase during REM sleep (awake: 91.77 ± 8.42; stage N1: 83.95 ± 11.05; stage N2: 71.71 ± 11.99; stage N3: 42.41 ± 9.14; REM: 80.11 ± 8.73). Mean and median BIS values were lower in the post-deprivation night than in the baseline night, showing statistical differences for the slow wave sleep (baseline: 42.41 ± 9.14 vs. post-deprivation: 39.49 ± 10.27; p = 0.02). BIS scores were able to discriminate properly between deep (N3) and light (N1, N2) sleep. BIS values during REM overlapped those of other sleep stages, although EMG activity provided by the BIS monitor could help to identify REM sleep if needed. In conclusion, BIS monitors could provide a useful measure of sleep depth in especially particular situations such as intensive care units, and they could be used as an alternative for sleep monitoring in order to reduce PSG-derived costs and to increase capacity in ambulatory care.Postprint (author's final draft

MODELING SLEEP FRAGMENTATION IN POPULATIONS OF SLEEP HYPNOGRAMS

We introduce methods for the analysis of large populations of sleep architectures (hypnograms) that respect the 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. By applying these methods to the hypnograms of 5598 subjects from the Sleep Heart Health Study we: 1) provide the firrst analysis of sleep hypnogram data of such size and complexity in a community cohort with a 4-level comorbidity; 2) compare 5-state 20-transition-type sleep to 3-state 6-transition-type sleep for a check of feasibility and information-loss; 3) extend current approaches to multivariate survival data analysis to populations of time-to-transition processes; and 4) provide scalable solutions for data analyses required by the case study. This allows us to provide detailed new insights into the association between sleep apnea and sleep architecture. Supporting R as well as SAS code and data are included in the online supplementary materials

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed