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.

Sex Differences in Vagus Nerve Stimulation Effects on Rat Cardiovascular and Immune Systems.

BACKGROUND: Investigations into the benefits of vagus nerve stimulation (VNS) through pre-clinical and clinical research have led to promising findings for treating several disorders. Despite proven effectiveness of VNS on conditions such as epilepsy and depression, understanding of off-target effects and contributing factors such as sex differences can be beneficial to optimize therapy design. NEW METHODS: In this article, we assessed longitudinal effects of VNS on cardiovascular and immune systems, and studied potential sex differences using a rat model of long-term VNS. Rats were implanted with cuff electrodes around the left cervical vagus nerve for VNS, and wireless physiological monitoring devices for continuous monitoring of cardiovascular system using electrocardiogram (ECG) signals. ECG morphology and heart rate variability (HRV) features were extracted to assess cardiovascular changes resulting from VNS in short-term and long-term timescales. We also assessed VNS effects on expression of inflammatory cytokines in blood during the course of the experiment. Statistical analysis was performed to compare results between Treatment and Sham groups, and between male and female animals from Treatment and Sham groups. RESULTS: Considerable differences between male and female rats in cardiovascular effects of VNS were observed in multiple cardiovascular features. However, the effects seemed to be transient with approximately 1-h recovery after VNS. While short-term cardiovascular effects were mainly observed in male rats, females in general showed more significant long-term effects even after VNS stopped. We did not observe notable changes or sex differences in systemic cytokine levels resulting from VNS. COMPARISON WITH EXISTING METHODS: Compared to existing methods, our study design incorporated wireless physiological monitoring and systemic blood cytokine level analysis, along with long-term VNS experiments in unanesthetized rats to study sex differences. CONCLUSION: The contribution of sex differences for long-term VNS off-target effects on cardiovascular and immune systems was assessed using awake behaving rats. Although VNS did not change the concentration of inflammatory biomarkers in systemic circulation for male and female rats, we observed significant differences in cardiovascular effects of VNS characterized using ECG morphology and HRV analyses.

Vagus Nerve Stimulation Unequally Disturbs Circadian Variation of Cardiac Rhythms in Male and Female Rats.

Vagus nerve stimulation (VNS) is a neurostimulation therapy for epilepsy and severe depression and has been recently shown to be effective for other conditions. Despite its demonstrated safety and efficacy, long-term and off-target effects of VNS remain to be fully determined. One of the complications reported in epilepsy is stimulation-induced sleep abnormalities. As epilepsy itself can impact sleep quality, contribution of VNS alone in such off-target effects remain mainly unknown. In this study, we analyzed data from long-term VNS experiments in rats to characterize effects of VNS on circadian rhythms derived from heart rate and heart rate variability (HRV). We have also explored possible sex differences in long-term effects of VNS on intrinsic biological rhythms. Compared with control animals, significant VNS-induced changes in circadian rhythms were observed particularly in female rats over 24h and 6h light cycles (1PM-7PM). These findings enhance our understanding of VNS contribution and biological sex role on sleep difficulties reported by using VNS therapy.

A framework to characterize the performance of early warning index alarm systems for patient monitoring.

In [Scully, C.G., and Daluwatte, C., Evaluating performance of early warning indices to predict physiological instabilities. J Biomed Inform. 75 (2017) 14-21], a framework was presented to characterize the performance of warning indices to provide information on the 1) probability a critical health event will occur when a warning is given (analogous to positive predictive value) and 2) proportion of warned events to all events (analogous to sensitivity). This framework also provides information about the timeliness of the warnings with respect to event occurrence and the warning burden of the system. •In the current work, we provide information on how this framework can be used when cases without events are present in a dataset to examine the proportion of warned non-events to all non-events (analogous to false positive rate).•Information on steps to apply the method, software, data and results for the case study are also provided to enable implementation of the framework.•Application and extension of the framework is demonstrated and discussed by adding non-event records to our previous case study comparing two warning strategies to predict physiologic instabilities.

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine.

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

Effect of Vigilance Changes on the Incidence of High Frequency Oscillations in the Epileptic Brain.

Recent studies show that the rate of cortical high frequency oscillations (HFOs) differentiates epileptogenic tissue in individuals with epilepsy. However, HFO occurrence can vary widely with vigilance state. In this study we attempt to characterize this variation, which has implications for the choice of a suitable diagnostic baseline for spatiotemporal analysis of HFO activity. We analyzed simultaneous recordings of the scalp electroencephalogram (EEG) and the electrocorticogram (ECoG) to examine the correlation of HFO activity with vigilance state. We detected HFOs (80-500 Hz) from all bipolar ECoG derivations using the well-known Staba algorithm in ten seizure-free overnight recordings from five patients being evaluated for surgery. In addition, we classified EEG features using a linkage tree into four vigilance states representing gradations in sleep depth from wakefulness to slow wave sleep. Finally, we examined the correlation between vigilance state and HFO occurrence in the five channels with the most HFOs in each recording. The proportion of 30-s epochs containing HFOs was found to increase significantly with sleep depth (p<0.01). Further analysis is necessary to examine the effects of epoch length and sample size in the choice of diagnostic baseline.

Sleep Depth Enhancement Through Ambient Temperature Manipulation in Mice.

The restorative properties of deep sleep and its central role in learning and memory are well-recognized but still in the process of being elucidated with the help of animal models. Currently available approaches for deep sleep enhancement are mainly pharmacological and may have undesirable side effects on physiology and behavior. Here, we propose a simple strategy for sleep depth enhancement that involves manipulation of ambient temperature (Ta) using a closed-loop control system. Even mild shifts in Ta are known to evoke thermoregulatory responses that alter sleep-wake dynamics. In our experiments, mice evinced greater proportions of deep NREM sleep as well as REM sleep under the dynamic sleep depth modulation protocol compared to a reference baseline in which Ta was left unchanged. The active manipulation approach taken in this study could be used as a more natural means for enhancing deep sleep in patients with disorders like epilepsy, Alzheimer's disease and Parkinson's, in which poor quality sleep is common and associated with adverse outcomes.

Short-Time Fourier Transform Based Spike Detection of Spontaneous Peripheral Nerve Activity.

Peripheral nerve interfaces are designed to record neural activity from residual nerves in amputees. Reliable detection of neural events from these recordings dictate the performance of neuroprosthetic device control. Extraction of neural events from peripheral nerve recordings is challenging because of low signal to noise ratio (SNR), sparse spiking pattern and the presence of electromyographic signal contamination from the surrounding muscles. In this study, we developed a spike detection algorithm based on Short-time Fourier Transform (STFT) and compared its performance to simple thresholding technique using synthesized nerve recordings. To mimic peripheral nerve recordings and produce ground-truth for validation, a quasi-simulation framework is proposed to incrementally synthesize signals from physiological recordings. A detection threshold was optimized on the spectral features of simulated signals and performance evaluation was done using an independent simulated data set. Results show that the STFT based technique, compared to the simple thresholding, reduces the false detection rate even in recordings with moderately low SNR.

Multivariate physiological recordings in an experimental hemorrhage model.

In this paper we describe a data set of multivariate physiological measurements recorded from conscious sheep (N = 8; 37.4 ± 1.1 kg) during hemorrhage. Hemorrhage was experimentally induced in each animal by withdrawing blood from a femoral artery at two different rates (fast: 1.25 mL/kg/min; and slow: 0.25 mL/kg/min). Data, including physiological waveforms and continuous/intermittent measurements, were transformed to digital file formats (European Data Format [EDF] for waveforms and Comma-Separated Values [CSV] for continuous and intermittent measurements) as a comprehensive data set and stored and publicly shared here (Appendix A). The data set comprises experimental information (e.g., hemorrhage rate, animal weight, event times), physiological waveforms (arterial and central venous blood pressure, electrocardiogram), time-series records of non-invasive physiological measurements (SpO2, tissue oximetry), intermittent arterial and venous blood gas analyses (e.g., hemoglobin, lactate, SaO2, SvO2) and intermittent thermodilution cardiac output measurements. A detailed explanation of the hemodynamic and pulmonary changes during hemorrhage is available in a previous publication (Scully et al., 2016) [1].

Applying a Computational Model Credibility Framework to Physiological Closed-loop Controlled Medical Device Testing.

Physiological closed-loop controlled medical devices are safety-critical systems that combine patient monitors with therapy delivery devices to automatically titrate therapy to meet a patient's current need. Computational models of physiological systems can be used to test these devices and generate pre-clinical evidence of safety and performance before using the devices on patients. The credibility, utility, and acceptability of such model-based test results will depend on, among other factors, the computational model used. We examine how a recently developed risk-informed framework for establishing the credibility of computational models in medical device applications can be applied in the evaluation of physiological closed-loop controlled devices.

Progression and variability of physiologic deterioration in an ovine model of lung infection sepsis.

In this study, a lung infection model of pneumonia in sheep (n = 12) that included smoke inhalation injury followed by methicillin-resistant Staphylococcus aureus placement into the lungs was used to investigate hemodynamic and pulmonary dysfunctions during the course of sepsis progression. To assess the variability in disease progression, animals were retrospectively divided into survivor (n = 6) and nonsurvivor (n = 6) groups, and a range of physiological indexes reflecting hemodynamic and pulmonary function were estimated and compared to evaluate variability in dynamics underlying sepsis development. Blood pressure and heart rate variability analyses were performed to assess whether they discriminated between the survivor and nonsurvivor groups early on and after intervention. Results showed hemodynamic deterioration in both survivor and nonsurvivor animals during sepsis along with a severe oxygenation disruption (decreased peripheral oxygen saturation) in nonsurvivors separating them from survivor animals of this model. Variability analysis of beat-to-beat heart rate and blood pressure reflected physiologic deterioration during infection for all animals, but these analyses did not discriminate the nonsurvivor animals from survivor animals.NEW & NOTEWORTHY Variable pulmonary response to injury results in varying outcomes in a previously reported animal model of lung injury and methicillin-resistant Staphylococcus aureus-induced sepsis. Heart rate and blood pressure variability analyses were investigated to track the varying levels of physiologic deterioration but did not discriminate early nonsurvivors from survivors.

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.

SegWay: A simple framework for unsupervised sleep segmentation in experimental EEG recordings.

Sleep analysis in animal models typically involves recording an electroencephalogram (EEG) and electromyogram (EMG) and scoring vigilance state in brief epochs of data as Wake, REM (rapid eye movement sleep) or NREM (non-REM) either manually or using a computer algorithm. Computerized methods usually estimate features from each epoch like the spectral power associated with distinctive cortical rhythms and dissect the feature space into regions associated with different states by applying thresholds, or by using supervised/unsupervised statistical classifiers; but there are some factors to consider when using them:•Most classifiers require scored sample data, elaborate heuristics or computational steps not easily reproduced by the average sleep researcher, who is the targeted end user.•Even when prediction is reasonably accurate, small errors can lead to large discrepancies in estimates of important sleep metrics such as the number of bouts or their duration.•As we show here, besides partitioning the feature space by vigilance state, modeling transitions between the states can give more accurate scores and metrics. An unsupervised sleep segmentation framework, "SegWay", is demonstrated by applying the algorithm step-by-step to unlabeled EEG recordings in mice. The accuracy of sleep scoring and estimation of sleep metrics is validated against manual scores.

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor.

BACKGROUND: Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. NEW METHOD: Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. RESULTS: Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. COMPARISON WITH EXISTING METHODS: Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. CONCLUSIONS: This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

Tunable somatosensory stimulation for selective sleep restriction studies in rodents.

Many methods for sleep restriction in rodents have emerged, but most are intrusive, lack fine control, and induce stress. Therefore, a versatile, non-intrusive means of sleep restriction that can alter sleep in a controlled manner could be of great value in sleep research. In previous work, we proposed a novel system for closed-loop somatosensory stimulation based on mechanical vibration and applied it to the task of restricting Rapid Eye Movement (REM) sleep in mice [1]. While this system was effective, it was a crude prototype and did not allow precise control over the amplitude and frequency of stimulation applied to the animal. This paper details the progression of this system from a binary, "all-or-none" version to one that allows dynamic control over perturbation to accomplish graded, state-dependent sleep restriction. Its preliminary use is described in two applications: deep sleep restriction in rats, and REM sleep restriction in mice.

Effect of temperature on sleep regulation in an animal epilepsy model.

Besides recurring seizures, disordered sleep is common in individuals with epilepsy and may present as reduced sleep depth, altered proportions of different stages of sleep, intermittent arousal, and other phenomena. Sleep loss can in turn precipitate seizures, thus sustaining a vicious cycle. It is well known that changes in ambient temperature elicit thermoregulatory responses that alter the dynamics of sleep. As a first step toward therapeutic sleep modulation for epilepsy, we assessed the effect of elevated ambient temperature on sleep dynamics and seizure yield in the chronic pilocarpine mouse model of temporal lobe epilepsy. The results in a small sample indicate that temperature does in fact significantly alter the proportions and durations of each vigilance state in this model, with possibly correlated changes in seizure incidence. Manipulation of ambient temperature therefore offers a simple and relatively unobtrusive way of titrating sleep quality and perhaps alleviating the seizure burden in epilepsy.

A mathematical model of ultradian sleep-wake regulation in rodents.

Rodent models are widely used for the experimental analysis of sleep. While this is motivated by similarities in brain circuitry and electrophysiological rhythms, unlike the circadian sleep-wake cycle in humans, rodent sleep is polyphasic, containing multiple bouts of sleep and wake minutes to hours in duration over the course of a day. Each sleep bout is punctuated by several brief arousals several seconds to minutes long. Physiologically motivated mathematical models replicate the shorter timescale of arousal within sleep, but not the longer one representing prolonged wakefulness. Here, we adapt a previously published "flip-flop" model of human sleep to capture the ultradian alternation of sleep and wakefulness in mice on the longer timescale. The resulting model reproduces both the mean durations of alternating sleep and wake bouts as well as the circadian trends in their bout durations documented in our experiments on mice.

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations.

Validation of a closed-loop sensory stimulation technique for selective sleep restriction in mice.

Experimental manipulation of sleep in rodents is an important tool for analyzing the mechanisms of sleep and related disorders in humans. Sleep restriction systems have relied in the past on manual sensory stimulation and recently on more sophisticated automated means of delivering the same. The ability to monitor and track behavior through the electroencephalogram (EEG) and other modalities provides the opportunity to implement more selective sleep restriction that is targeted at particular stages of sleep with flexible control over their amount, duration, and timing. In this paper we characterize the performance of a novel tactile stimulation system operating in closed-loop to interrupt rapid eye movement (REM) sleep in mice when it is detected in real time from the EEG. Acute experiments in four wild-type mice over six hours showed that a reduction of over 50% of REM sleep was feasible without affecting non-REM (NREM) sleep. The animals remained responsive to the stimulus over the six hour duration of the experiment.

Naive scoring of human sleep based on a hidden Markov model of the electroencephalogram.

Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).