Automated analysis of sleep-wake state in rats

Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues

There is nothing we spend as much time on in our lives as we do sleeping, which makes it even more surprising that we currently do not know why we need to sleep. Most of the research addressing this question is performed in rodents to allow for invasive, mechanistic approaches. However, in contrast to human sleep, we currently do not have shared and agreed upon standards on sleep states in rodents. In this article, we present an overview on sleep stages in humans and rodents and a historical perspective on the development of automatic sleep scoring systems in rodents. Further, we highlight specific issues in rodent sleep that also call into question some of the standards used in human sleep research.

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

A novel machine learning system for identifying sleep-wake states in mice

Research into sleep-wake behaviors relies on scoring sleep states, normally done by manual inspection of electroencephalogram (EEG) and electromyogram (EMG) recordings. This is a highly time-consuming process prone to inter-rater variability. When studying relationships between sleep and motor function, analyzing arousal states under a four-state system of active wake (AW), quiet wake (QW), nonrapid-eye-movement (NREM) sleep, and rapid-eye-movement (REM) sleep provides greater precision in behavioral analysis but is a more complex model for classification than the traditional three-state identification (wake, NREM, and REM sleep) usually used in rodent models. Characteristic features between sleep-wake states provide potential for the use of machine learning to automate classification. Here, we devised SleepEns, which uses a novel ensemble architecture, the time-series ensemble. SleepEns achieved 90% accuracy to the source expert, which was statistically similar to the performance of two other human experts. Considering the capacity for classification disagreements that are still physiologically reasonable, SleepEns had an acceptable performance of 99% accuracy, as determined blindly by the source expert. Classifications given by SleepEns also maintained similar sleep-wake characteristics compared to expert classifications, some of which were essential for sleep-wake identification. Hence, our approach achieves results comparable to human ability in a fraction of the time. This new machine-learning ensemble will significantly impact the ability of sleep researcher to detect and study sleep-wake behaviors in mice and potentially in humans.

Application of Machine Learning to Sleep Stage Classification

Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often, investigators manually classify the polysomnography into vigilance states, which is time-consuming, requires extensive training, and is prone to inter-scorer variability. While many works have successfully developed automated vigilance state classifiers based on multiple EEG channels, we aim to produce an automated and openaccess classifier that can reliably predict vigilance state based on a single cortical electroencephalogram (EEG) from rodents to minimize the disadvantages that accompany tethering small animals via wires to computer programs. Approximately 427 hours of continuously monitored EEG, electromyogram (EMG), and activity were labeled by a domain expert out of 571 hours of total data. Here we evaluate the performance of various machine learning techniques on classifying 10-second epochs into one of three discrete classes: paradoxical, slow-wave, or wake. Our investigations include Decision Trees, Random Forests, Naive Bayes Classifiers, Logistic Regression Classifiers, and Artificial Neural Networks. These methodologies have achieved accuracies ranging from approximately 74% to approximately 96%. Most notably, the Random Forest and the ANN achieved remarkable accuracies of 95.78% and 93.31%, respectively. Here we have shown the potential of various machine learning classifiers to automatically, accurately, and reliably classify vigilance states based on a single EEG reading and a single EMG reading.

An artificial neural network for automated behavioral state classification in rats

Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.

A real-time sleep scoring framework for closed-loop sleep manipulation in mice

Subtle changes in sleep architecture can accompany and be symptomatic of many diseases or disorders. In order to probe and understand the complex interactions between sleep and health, the ability to model, track, and modulate sleep in preclinical animal models is vital. While various methods have been described for scoring experimental sleep recordings, few are designed to work in real time - a prerequisite for closed-loop sleep manipulation. In the present study, we have developed algorithms and software to classify sleep in real time and validated it on C57BL/6 mice (n = 8). Hidden Markov models of baseline sleep dynamics were fitted using an unsupervised algorithm to electroencephalogram (EEG) and electromyogram (EMG) data for each mouse, and were able to classify sleep in a manner highly concordant with manual scoring (Cohen's Kappa >75%) up to 3 weeks after model construction. This approach produced reasonably accurate estimates of common sleep metrics (proportion, mean duration, and number of bouts). After construction, the models were used to track sleep in real time and accomplish selective rapid eye movement (REM) sleep restriction by triggering non-invasive somatosensory stimulation. During REM restriction trials, REM bout duration was significantly reduced, and the classifier continued to perform satisfactorily despite the disrupted sleep patterns. The software can easily be tailored for use with other commercial or customised methods of sleep disruption (e.g. stir bar, optogenetic stimulation, etc.) and could serve as a robust platform to facilitate closed-loop experimentation. The source code and documentation are freely available upon request from the authors.

Identification of the sleep-wake states in rats using the high-frequency activity of the electroencephalogram

The electroencephalographic signal constitutes the main sign classically used for the identification of states of alertness. However, activities in the high frequency (>100 Hz) range have not been properly studied despite their high potential for sleep scoring in rodents. In the present study, we designed a method for the identification of the sleep-wake states in rats by exclusively using high-frequency activities of the electroencephalogram. By calculating the ratio between the amplitude of the electroencephalographic signal from 110 to 200 Hz and from 110 to 300 Hz, we obtained an index that had values that were low during wakefulness, intermediate during non-REM sleep and high during REM sleep. This high-frequency index (HiFI) allowed the identification of each state without the need to study other signs such as muscle activity or eye movements. To evaluate the performance of the index, we compared it with the conventional scoring of the sleep-wake cycle based upon the study of the electromyogram and delta (0.5-4 Hz), theta (6-9 Hz) and sigma (10-14 Hz) bands of the electroencephalogram. The index had an accuracy of 90.43 ± 1.91% (Cohen's kappa value of 0.82), confirming that the study of the high-frequency activities of the electroencephalogram was sufficient to reliably identify alertness states in the rat. Compared to other sleep-scoring methods, the HiFI has several advantages. It only requires one electroencephalography electrode, thus reducing the severity of the surgical preparation of the experimental animal, and its calculation is very simple, so it can be easily implemented online to classify sleep-wake states in real time.

Differential effects of traxoprodil and S-ketamine on quantitative EEG and auditory event-related potentials as translational biomarkers in preclinical trials in rats and mice

Quantitative Electroencephalography (qEEG) and event-related potential (ERP) assessment have emerged as powerful tools to unravel translational biomarkers in preclinical and clinical psychiatric drug discovery trials. The aim of the present study was to compare the GluN2B negative allosteric modulator (NAM) traxoprodil (CP-101,606) with the unselective NMDA receptor channel blocker S-ketamine to give insight into central target engagement and differentiation on multiple EEG readouts. For qEEG recordings telemetric transmitters were implanted in male Wistar rats. Recorded EEG data were analyzed using fast Fourier transformation to determine power spectra and vigilance states. Additionally, body temperature and locomotor activity were assessed via telemetry. For recordings of auditory event-related potentials (AERP) male C57Bl/6J mice were chronically implanted with deep electrodes using a tethered system. Power spectral analysis revealed a significant increase in gamma power following ketamine treatment, whereas traxoprodil (6&18 mg/kg) induced an overall decrease primarily within alpha and beta bands. Additionally, ketamine disrupted sleep and enhanced time spent in wake vigilance states, whereas traxoprodil did not alter sleep-wake architecture. AERP and mismatch negativity (MMN) revealed that ketamine (10 mg/kg) selectively disrupts auditory deviance detection, whereas traxoprodil (6 mg/kg) did not alter MMN at clinically relevant doses. In contrast to ketamine treatment, traxoprodil did not produce hyperactivity and hypothermia. In conclusion, ketamine and traxoprodil showed very different effects on diverse EEG readouts differentiating selective GluN2B antagonism from non-selective pan-NMDA-R antagonists like ketamine. These readouts are thus perfectly suited to support drug discovery efforts on NMDA-R and understanding the different functions of NMDA-R subtypes.

Robust, automated sleep scoring by a compact neural network with distributional shift correction

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

Development of a rule-based automatic five-sleep-stage scoring method for rats

Background

Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake–sleep stages in rodent models. The present study aimed to develop and validate an automatic rule-based classification of 5-stage wake–sleep pattern in acid-induced widespread hyperalgesia model of the rat.

Results

The overall agreement between two experts’ consensus and automatic scoring in the 5-stage and 3-stage analyses were 92.32% (κ = 0.88) and 94.97% (κ = 0.91), respectively. Standard deviation of the accuracy among all rats was only 2.93%. Both frontal–occipital EEG and parietal EEG data showed comparable accuracies. The results demonstrated the performance of the proposed method with high accuracy and reliability. Subtle changes exhibited in the 5-stage wake–sleep analysis but not in the 3-stage analysis during hyperalgesia development of the acid-induced pain model. Compared with existing methods, our method can automatically classify vigilance states into 5-stage or 3-stage wake–sleep pattern with a promising high agreement with sleep experts.

Conclusions

In this study, we have performed and validated a reliable automated sleep scoring system in rats. The classification algorithm is less computation power, a high robustness, and consistency of results. The algorithm can be implanted into a versatile wireless portable monitoring system for real-time analysis in the future.

Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore^TM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.

Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anaesthesia depth

Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance.

Real-time tracking of vigilance states related to wake, sleep, and anaesthesia has been a goal for over a century. However identification of wakefulness and different sleep states cannot currently be performed routinely with brain signals and instead relies on motor activity. Here we demonstrate that 50–70 Hz electrical oscillations in the olfactory bulb (OB) of mice are a reliable indicator for global brain states. Recording this activity with an implanted electrode allows for clear classification of sleep and wake, without the need for motor activity monitoring. We construct a fully automatic sleep scoring algorithm that relies on brain activity alone and is robust throughout time, between animals, and after drug administration. Our method also tracks in real time the depth of anaesthesia both in the steady state under constant anaesthetic and dynamically during the recovery period from anaesthesia. Furthermore, this index predicts responsiveness to noxious stimulation under anaesthesia. Altogether, this methodology opens the avenue for characterisation of vigilance states based on OB recordings.

Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

BACKGROUND

An automated process for sleep staging based on intracranial EEG data alone is needed to facilitate research into the neural processes occurring during slow wave sleep (SWS). Current manual methods for sleep scoring require a full polysomnography (PSG) set-up, including electrooculography (EOG), electromyography (EMG), and scalp electroencephalography (EEG). This set-up can be technically difficult to place in the presence of intracranial EEG electrodes. There is thus a need for a method for sleep staging based on intracranial recordings alone.

NEW METHOD

Here we show a reliable automated method for the detection of periods of SWS solely based on intracranial EEG recordings. The method utilizes the ratio of spectral power in delta, theta, and spindle frequencies relative to alpha and beta frequencies to classify 30-s segments as SWS or not.

RESULTS

We evaluated this new method by comparing its performance against visually scored patients (n=9), in which we also recorded EOG and EMG simultaneously. Our method had a mean positive predictive value of 64% across all nights. Also, an ROC analysis of the performance of our algorithm compared to manually labeled nights revealed a mean average area under the curve of 0.91 across all nights.

COMPARISON WITH EXISTING METHOD

Our method had an average kappa score of 0.72 when compared to visual sleep scoring by an independent blinded sleep scorer.

CONCLUSION

This shows that this simple method is capable of differentiating between SWS and non-SWS epochs reliably based solely on intracranial EEG recordings.

Investigation of sleep-wake rhythm in non-human primates without restraint during data collection

To understand sleep mechanisms and develop treatments for sleep disorders, investigations using animal models are essential. The sleep architecture of rodents differs from that of diurnal mammals including humans and non-human primates. Sleep studies have been conducted in non-human primates; however, these sleep assessments were performed on animals placed in a restraint chair connected via the umbilical area to the recording apparatus. To avoid restraints, cables, and other stressful apparatuses and manipulations, telemetry systems have been developed. In the present study, sleep recordings in unrestrained cynomolgus monkeys (Macaca fascicularis) and common marmoset monkeys (Callithrix jacchus) were conducted to characterize normal sleep. For the analysis of sleep-wake rhythms in cynomolgus monkeys, telemetry electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) signals were used. For the analysis of sleep-wake rhythms in marmosets, telemetry EEG and EOG signals were used. Both monkey species showed monophasic sleep patterns during the dark phase. Although non-rapid eye movement (NREM) deep sleep showed higher levels at the beginning of the dark phase in cynomolgus monkeys, NREM deep sleep rarely occurred during the dark phase in marmosets. Our results indicate that the use of telemetry in non-human primate models is useful for sleep studies, and that the different NREM deep sleep activities between cynomolgus monkeys and common marmoset monkeys are useful to examine sleep functions.

Significance of the zero sum principle for circadian, homeostatic and allostatic regulation of sleep-wake state in the rat

Sleep-wake behavior exhibits diurnal rhythmicity, rebound responses to acute total sleep deprivation (TSD), and attenuated rebounds following chronic sleep restriction (CSR). We investigated how these long-term patterns of behavior emerge from stochastic short-term dynamics of state transition. Male Sprague-Dawley rats were subjected to TSD (1day×24h, N=9), or CSR (10days×18h TSD, N=7) using a rodent walking-wheel apparatus. One baseline day and one recovery day following TSD and CSR were analyzed. The implications of the zero sum principle were evaluated using a Markov model of sleep-wake state transition. Wake bout duration (a combined function of the probability of wake maintenance and proportional representations of brief and long wake) was a key variable mediating the baseline diurnal rhythms and post-TSD responses of all three states, and the attenuation of the post-CSR rebounds. Post-NREM state transition trajectory was an important factor in REM rebounds. The zero sum constraint ensures that a change in any transition probability always affects bout frequency and cumulative time of at least two, and usually all three, of wakefulness, NREM and REM. Neural mechanisms controlling wake maintenance may play a pivotal role in regulation and dysregulation of all three states.

Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements

The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman’s rho 0.43–0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

Twenty-four hours hypothermia has temporary efficacy in reducing brain infarction and inflammation in aged rats

Stroke is a major cause of disability for which no neuroprotective measures are available. Age is the principal nonmodifiable risk factor for this disease. Previously, we reported that exposure to hydrogen sulfide for 48 hours after stroke lowers whole body temperature and confers neuroprotection in aged animals. Because the duration of hypothermia in most clinical trials is between 24 and 48 hours, we questioned whether 24 hours exposure to gaseous hypothermia confers the same neuroprotective efficacy as 48 hours exposure. We found that a shorter exposure to hypothermia transiently reduced both inflammation and infarct size. However, after 1 week, the infarct size became even larger than in controls and after 2 weeks there was no beneficial effect on regenerative processes such as neurogenesis. Behaviorally, hypothermia also had a limited beneficial effect. Finally, after hydrogen sulfide-induced hypothermia, the poststroke aged rats experienced a persistent sleep impairment during their active nocturnal period. Our data suggest that cellular events that are delayed by hypothermia in aged rats may, in the long term, rebound, and diminish the beneficial effects.

Unsupervised online classifier in sleep scoring for sleep deprivation studies

STUDY OBJECTIVE

This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents.

DESIGN

We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD).

SETTINGS

Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD.

PARTICIPANTS

Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings.

MEASUREMENTS

The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours.

RESULTS

Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes).

CONCLUSIONS

Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method.

Behavioral sleep-wake homeostasis and EEG delta power are decoupled by chronic sleep restriction in the rat

STUDY OBJECTIVES

Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR.

DESIGN

A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function.

SETTING

Animal laboratory, rodent walking-wheel apparatus.

SUBJECTS

Male Sprague-Dawley rats.

INTERVENTIONS

Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6).

MEASUREMENTS AND RESULTS

Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR.

CONCLUSIONS

We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.

STUDY OBJECTIVES

Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR.

DESIGN

A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function.

SETTING

Animal laboratory, rodent walking-wheel apparatus.

SUBJECTS

Male Sprague-Dawley rats.

INTERVENTIONS

Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6).

MEASUREMENTS AND RESULTS

Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR.

CONCLUSIONS

We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.

Sleep scoring made easy-Semi-automated sleep analysis software and manual rescoring tools for basic sleep research in mice

Studying sleep behavior in animal models demands clear separation of vigilance states. Pure manual scoring is time-consuming and commercial scoring software is costly. We present a LabVIEW-based, semi-automated scoring routine using recorded EEG and EMG signals. This scoring routine is •designed to reliably assign the vigilance/sleep states wakefulness (WAKE), non-rapid eye movement sleep (NREMS) and rapid eye movement sleep (REMS) to defined EEG/EMG episodes.•straightforward to use even for beginners in the field of sleep research.•freely available upon request. Chronic recordings from mice were used to design and evaluate the scoring routine consisting of an artifact-removal, a scoring- and a rescoring routine. The scoring routine processes EMG and different EEG frequency bands. Amplitude-based thresholds for EEG and EMG parameters trigger a decision tree assigning each EEG episode to a defined vigilance/sleep state automatically. Using the rescoring routine individual episodes or particular state transitions can be re-evaluated manually. High agreements between auto-scored and manual sleep scoring could be shown for experienced scorers and for beginners quickly and reliably. With small modifications to the software, it can be easily adapted for sleep analysis in other animal models.

An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters

INTRODUCTION

Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.

METHODS

We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.

RESULTS

More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.

CONCLUSIONS

Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

Intensive care unit depth of sleep: proof of concept of a simple electroencephalography index in the non-sedated

INTRODUCTION

Intensive care unit (ICU) patients are known to experience severely disturbed sleep, with possible detrimental effects on short- and long- term outcomes. Investigation into the exact causes and effects of disturbed sleep has been hampered by cumbersome and time consuming methods of measuring and staging sleep. We introduce a novel method for ICU depth of sleep analysis, the ICU depth of sleep index (IDOS index), using single channel electroencephalography (EEG) and apply it to outpatient recordings. A proof of concept is shown in non-sedated ICU patients.

METHODS

Polysomnographic (PSG) recordings of five ICU patients and 15 healthy outpatients were analyzed using the IDOS index, based on the ratio between gamma and delta band power. Manual selection of thresholds was used to classify data as either wake, sleep or slow wave sleep (SWS). This classification was compared to visual sleep scoring by Rechtschaffen & Kales criteria in normal outpatient recordings and ICU recordings to illustrate face validity of the IDOS index.

RESULTS

When reduced to two or three classes, the scoring of sleep by IDOS index and manual scoring show high agreement for normal sleep recordings. The obtained overall agreements, as quantified by the kappa coefficient, were 0.84 for sleep/wake classification and 0.82 for classification into three classes (wake, non-SWS and SWS). Sensitivity and specificity were highest for the wake state (93% and 93%, respectively) and lowest for SWS (82% and 76%, respectively). For ICU recordings, agreement was similar to agreement between visual scorers previously reported in literature.

CONCLUSIONS

Besides the most satisfying visual resemblance with manually scored normal PSG recordings, the established face-validity of the IDOS index as an estimator of depth of sleep was excellent. This technique enables real-time, automated, single channel visualization of depth of sleep, facilitating the monitoring of sleep in the ICU.

Statistical properties of sleep-wake behavior in the rat and their relation to circadian and ultradian phases

STUDY OBJECTIVES

To examine the statistical characteristics of short-term sleep-wake architecture and to evaluate their dependence on ultradian and circadian phase.

DESIGN

Observational, time series.

SETTING

Laboratory.

PARTICIPANTS

Ten male adult Sprague-Dawley rats.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

States of wakefulness (WAKE), rapid eye movement sleep (REM) and nonrapid eye movement sleep (NREM) were recorded in 5-sec epochs over 7 consecutive days. State bout durations were analyzed using parametric regression of survival curves, comparing exponential, biexponential, and power law models. WAKE survival curves were best fit by biexponential models, suggesting that there are two statistically distinct stochastic mechanisms generating two types of WAKE--"brief" WAKE and "long" WAKE. Exponential time constants varied as a function of circadian and ultradian phase, with "long" WAKE showing the largest effect. NREM survival curves exhibited biexponential and monoexponential distributions in light and dark, respectively, with weak effects of ultradian phase. REM survival curves approximated a monoexponential distribution that varied with circadian but not ultradian phase. χ(2) analysis was used in a three-state Markov model to evaluate whether conditional state transition probabilities exhibit the property of first-order dependence. This was partially confirmed, but only after accounting for heterogeneity associated with circadian and ultradian phase. However, there was evidence of residual second-order dependence indicating that additional sources of statistical heterogeneity may remain to be identified.

CONCLUSIONS

Sleep-wake state is regulated over short timescales by stochastic mechanisms. When the major sources of heterogeneity are taken into account, including two-component WAKE and NREM states, the sleep-wake system of the rat behaves, to a reasonable approximation, as a Markovian system that is modulated over ultradian and circadian timescales.

Sleep-wake behavior in the rat: ultradian rhythms in a light-dark cycle and continuous bright light

Ultradian rhythms are a prominent but little-studied feature of mammalian sleep-wake and rest-activity patterns. They are especially evident in long-term records of behavioral state in polyphasic animals such as rodents. However, few attempts have been made to incorporate ultradian rhythmicity into models of sleep-wake dynamics, and little is known about the physiological mechanisms that give rise to ultradian rhythms in sleep-wake state. This study investigated ultradian dynamics in sleep and wakefulness in rats entrained to a 12-h:12-h light-dark cycle (LD) and in rats whose circadian rhythms were suppressed and free-running following long-term exposure to uninterrupted bright light (LL). We recorded sleep-wake state continuously for 7 to 12 consecutive days and used time-series analysis to quantify the dynamics of net cumulative time in each state (wakefulness [WAKE], rapid eye movement sleep [REM], and non-REM sleep [NREM]) in each animal individually. Form estimates and autocorrelation confirmed the presence of significant ultradian and circadian rhythms; maximum entropy spectral analysis allowed high-resolution evaluation of multiple periods within the signal, and wave-by-wave analysis enabled a statistical evaluation of the instantaneous period, peak-trough range, and phase of each ultradian wave in the time series. Significant ultradian periodicities were present in all 3 states in all animals. In LD, ultradian range was approximately 28% of circadian range. In LL, ultradian range was slightly reduced relative to LD, and circadian range was strongly attenuated. Ultradian rhythms were found to be quasiperiodic in both LD and LL. That is, ultradian period varied randomly around a mean of approximately 4 h, with no relationship between ultradian period and time of day.

Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths

Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a night's sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincaré plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.

Automated sleep scoring in rats and mice using the naive Bayes classifier

We describe a new simple MATLAB-based method for automated scoring of rat and mouse sleep using the naive Bayes classifier. This method is highly sensitive resulting in overall auto-rater agreement of 93%, comparable to an inter-rater agreement between two human scorers (92%), with high sensitivity and specificity values for wake (94% and 96%), NREM sleep (94% and 97%) and REM sleep (89% and 97%) states. In addition to baseline sleep-wake conditions, the performance of the naive Bayes classifier was assessed in sleep deprivation and drug infusion experiments, as well as in aged and transgenic animals using multiple EEG derivations. 24-h recordings from 30 different animals were used, with approximately 5% of the data manually scored as training data for the classification algorithm.

Energy expenditure is affected by rate of accumulation of sleep deficit in rats

STUDY OBJECTIVES

Short sleep is a putative risk factor for obesity. However, prolonged total sleep deprivation (TSD) leads to negative energy balance and weight loss in rodents, whereas sleep-restricted humans tend to gain weight. We hypothesized that energy expenditure (VO2) is influenced by the rate of accumulation of sleep deficit in rats.

DESIGN AND INTERVENTION

Six Sprague-Dawley rats underwent chronic sleep-restriction (CSR, 6-h sleep opportunity at ZT0-6 for 10 days) and stimulus-control protocols (CON, 12-h sleep opportunity for 10 days, matched number of stimuli) in a balanced cross-over design. Four additional rats underwent TSD (4 days). Sleep was manipulated using a motor-driven walking wheel.

MEASUREMENTS AND RESULTS

Electroencephalography, electromyography, and body temperature were measured by telemetry, and VO2, by respirometry. Total sleep deficits of 55.1 +/- 6.4 hours, 31.8 +/- 6.8 hours, and 38.2 +/- 2.3 hours accumulated over the CSR, CON, and TSD protocols, respectively. Responses to TSD confirmed previous reports of elevated VO2 and body temperature. These responses were attenuated in CSR, despite a greater cumulative sleep deficit. Rate of rise of VO2 was strongly correlated with rate of accumulation of sleep deficit, above a threshold deficit of 3.6 h x day(-1).

CONCLUSION

The change in VO2 is affected by rate of accumulation of sleep deficit and not the total sleep loss accrued. Negative energy balance, observed during TSD, is strongly attenuated when brief daily sleep opportunities are available to rats (CSR), despite greater accumulated sleep deficit.

Manual rat sleep classification in principal component space

A simple method is described for using principal component analysis (PCA) to score rat sleep recordings as awake, rapid-eye-movement (REM) sleep, or non-REM (NREM) sleep. PCA was used to reduce the dimensionality of the features extracted from each epoch to three, and the projections were then graphed in a scatterplot where the clusters were visually apparent. The clusters were then directly manually selected, classifying the entire recording at once. The method was tested in a set of ten 24-h rat sleep electroencephalogram (EEG) and electromyogram (EMG) recordings. Classifications by two human raters performing traditional epoch-by-epoch scoring were blindly compared with classifications by another two human raters using the new PCA method. Overall inter-rater median percent agreements ranged between 93.7% and 94.9%. Median Cohen's kappa coefficient ranged from 0.890 to 0.909. The PCA method on average required about 5 min for classification of each 24-h recording. The combination of good accuracy and reduced time compared to traditional sleep scoring suggests that the method may be useful for sleep research.