Wake Epochs Research Articles

Stability of infraslow correlation structure in time-shifted intracranial EEG signals.

It is increasingly understood that the epilepsies are characterized by network pathology that can span multiple spatial and temporal scales. Recent work indicates that infraslow (<0.2Hz) envelope correlations may form a basis for distant spatial coupling in the brain. We speculated that infraslow correlation structure may be preserved even with some time lag between signals. To this end, we studied intracranial EEG (icEEG) data collected from 22 medically refractory epilepsy patients. For each patient, we selected hour-long background, awake icEEG epochs before and after antiseizure medication (ASM) taper. For each epoch, we selected 5,000 random electrode contact pairs and estimated magnitude-squared coherence (MSC) below 0.15Hz of band power time-series in the traditional EEG frequency bands. Using these same contact pairs, we shifted one signal of the pair by random durations in 15-s increments between 0 and 300s. We aggregated these data across all patients to determine how infraslow MSC varies with duration of lag. We further examined the effect of ASM taper on infraslow correlation structure. We also used surrogate data to empirically characterize MSC estimator and to set optimal parameters for estimation specifically for the study of infraslow activity. Our empirical analysis of the MSC estimator showed that hour-long segments with MSC computed using 3-min windows with 50% overlap was sufficient to capture infraslow envelope correlations while minimizing estimator bias and variance. The mean MSC decreased monotonically with increasing time lag until 105s of lag, then plateaued between 106 and 300s. Significantly nonzero infraslow envelope MSC was preserved in all frequency bands until about 1min of time lag, both pre- and post-ASM taper. We also saw a slight, but significant increase in infraslow MSC post-ASM taper, consistent with prior work. These results provide evidence for the feasibility of examining infraslow activity via its modulation of higher-frequency activity in the absence of DC-coupled recordings. The use of surrogate data also provides a general methodology for benchmarking measures used in network neuroscience studies. Finally, our study points to the clinical relevance of infraslow activity in assessing seizure risk.

The impact of study type and sleep measurement on oxygen desaturation index calculation.

The oxygen desaturation index (ODI) is an important measure of sleep-disordered breathing during polysomnography (PSG); however, the AASM Manual (V3) does not specify whether to include oxygen desaturations occurring during wake epochs. Additionally, an ODI obtained from PSG can differ from an ODI using home sleep apnea tests (HSATs) that do not measure sleep, hampering diagnostic and treatment decision reliability. This study aimed to (1) compare an ODI that included all desaturations with an ODI that excluded desaturations occurring during wake epochs in PSG and (2) compare ODIs obtained from PSG with HSAT. 100 consecutive PSGs for investigation of obstructive sleep apnea were compared. ODIs were calculated including all desaturations (ODIall) and by excluding desaturations entirely during wake epochs (ODIsleep). Additionally, we compared ODIall with an ODI calculated using monitoring time as the denominator (ODIHSAT). The median (interquartile range) 3% ODI for ODIall was 22.8 (13.1, 44.1) events/h and ODIsleep was 17.6 (11.5, 35.2) events/h (median difference: -3.9 events/h [-8.2, -0.9]; 21.0% [8.7%, 33.2%]). This discrepancy was larger with increasing ODI and decreasing sleep efficiency. The ODIHSAT was 17.4 (11.3, 35.2) events/h and the median reduction in ODIHSAT vs ODIall was -4.5 (-10.9, -2.0) events/h (21.6%; 11.1%, 33.8). ODI was significantly reduced when desaturations in wake epochs were excluded, and when ODI was based on monitoring time rather than sleep time, with the potential for underestimation of disease severity. Results suggest that ODI can differ substantially depending on the calculation and study type used, and that there is a need for standardization to ensure consistent diagnosis and treatment outcomes. Whenn CB, Wilson DL, Ruehland WR, Churchward TJ, Worsnop C, Tolson J. The impact of study type and sleep measurement on oxygen desaturation index calculation. J Clin Sleep Med. 2024;20(5):709-717.

Prefrontal stimulation as a tool to disrupt hippocampal and striatal reactivations underlying fast motor memory consolidation

BackgroundRecent evidence suggests that hippocampal replay in humans support rapid motor memory consolidation during epochs of wakefulness interleaved with task practice. Objectives/HypothesesThe goal of this study was to test whether such reactivation patterns can be modulated with experimental interventions and in turn influence fast consolidation. We hypothesized that non-invasive brain stimulation targeting hippocampal and striatal networks via the prefrontal cortex would influence brain reactivation and the rapid form of motor memory consolidation. MethodsTheta-burst stimulation was applied to a prefrontal cluster functionally connected to both the hippocampus and striatum of young healthy participants before they learned a motor sequence task in a functional magnetic resonance imaging (fMRI) scanner. Neuroimaging data acquired during task practice and the interleaved rest epochs were analyzed to comprehensively characterize the effect of stimulation on the neural processes supporting fast motor memory consolidation. ResultsOur results collectively show that active, as compared to control, theta-burst stimulation of the prefrontal cortex hindered fast motor memory consolidation. Converging evidence from both univariate and multivariate analyses of fMRI data indicate that active stimulation disrupted hippocampal and caudate responses during inter-practice rest, presumably altering the reactivation of learning-related patterns during the micro-offline consolidation episodes. Last, stimulation altered the link between the brain and the behavioral markers of the fast consolidation process. ConclusionThese results suggest that stimulation targeting deep brain regions via the prefrontal cortex can be used to modulate hippocampal and striatal reactivations in the human brain and influence motor memory consolidation.

Objective assessment of sleep parameters using multimodal AX3 data in older participants

AbstractBackgroundSleep disturbances are both risk factors for and symptoms of dementia. Current methods for assessing sleep disturbances are largely based on either polysomnography (PSG) which is costly and inconvenient, or self‐ or care‐giver reports which are prone to measurement error. Low‐cost methods to monitor sleep disturbances longitudinally and at scale can be useful for assessing symptom development. Here, we develop deep learning models that use multimodal variables (accelerometers and temperature) recorded by the AX3 to accurately identify sleep and wake epochs and derive sleep parameters.MethodEighteen men and women (65‐80y) participated in a sleep laboratory‐based study in which multiple devices for sleep monitoring were evaluated. PSGs were recorded over a 10‐h period and scored according to established criteria per 30 sec epochs. Tri‐axial accelerometers and temperature signals were captured with an Axivity AX3, at 100Hz and 1Hz, respectively, throughout a 19‐h period, including 10‐h concurrent PSG recording and 9‐h of wakefulness. We developed and evaluated a supervised deep learning algorithm to detect sleep and wake epochs and determine sleep parameters from the multimodal AX3 raw data. We validated our results with gold standard PSG measurements and compared our algorithm to the Biobank accelerometer analysis toolbox. Single modality (accelerometer or temperature) and multimodality (both signals) approaches were evaluated using the 3‐fold cross‐validation.ResultThe proposed deep learning model outperformed baseline models such as the Biobank accelerometer analysis toolbox and conventional machine learning classifiers (Random Forest and Support Vector Machine) by up to 25%. Using multimodal data improved sleep and wake classification performance (up to 18% higher) compared with the single modality. In terms of the sleep parameters, our approach boosted the accuracy of estimations by 11% on average compared to the Biobank accelerometer analysis toolbox.ConclusionIn older adults without dementia, combining multimodal data from AX3 with deep learning methods allows satisfactory quantification of sleep and wakefulness. This approach holds promise for monitoring sleep behaviour and deriving accurate sleep parameters objectively and longitudinally from a low‐cost wearable sensor. A limitation of our current study is that the participants were healthy older adults: future work will focus on people living with dementia.

0308 Impact of Customized Lighting on Sleep in Hospitalized Patients

Abstract Introduction Inpatient sleep may be disrupted for myriad reasons, including lighting exposure that is typically fixed at constant intensity and spectra. In this study we employed customized lighting that promotes circadian entrainment by exposing patients to blue-augmented lighting during the morning and blue-suppressed lighting evening to improve sleep timing and duration. Methods Two Mount Sinai hospital rooms (with 2 beds each) were outfitted with Eight Mattress sensors that use a combination of movement, breathing, heart rate, and temperature variables to estimate wake and sleep vigilance states in 30-second epochs. One room contained customized lighting between 7 am and 11 pm, while the other room contained standard hospital lighting. The current analysis includes 60 participants (30 in the control room and 30 in the custom lighting room), who were matched for time-of-year hospital admission date. We first analyzed the probability of sleep during any given 30-second epoch across each subject’s admission and compared the distributions of sleep probability across the 24-hour cycle for all subjects in the lighting vs control conditions. We additionally evaluated total nocturnal sleep duration(11 PM – 7 AM) by 1) summating all sleep epochs on nights containing at least 50% valid data and 2) normalizing all measured sleep epochs to all valid sleep/wake outputs (excluding indeterminate epochs). Results The distribution of sleep probability was significantly different in the lighting vs control condition (p &amp;lt; 0.001) with most apparent diversion of the distributions between 9 pm and 9 am. The average raw nocturnal sleep duration was significantly higher in lighting condition (270.0 ± 23 minutes vs 201 ± 23 min in control, p = 0.037), as was the average minutes of sleep per hour of valid recording (36.9 ± 15 minutes vs 28.4 ± 13 in control, p = 0.022). There were no significant differences in the number of nocturnal wake or indeterminate epochs between conditions. Conclusion Custom lighting appears to promote sleep in hospitalized patients, but the interpretation is tempered by absence of full validation of the mattress sensor against polysomnography. Future work will evaluate differences in sleep timing and continuity between conditions. Support (if any) McClung Foundation, R01 AG066870

Evaluation of consensus sleep stage scoring of dysregulated sleep in Parkinson's disease

ObjectiveSleep dysregulation in Parkinson's disease (PD) has been hypothesized to occur, in part, from dysfunction in the basal ganglia-cortical circuit. Assessment of this relationship requires accurate sleep stage determination, a known challenge in this clinical population. Our objective was to optimize the consensus on the sleep staging process and reduce interrater variability in a cohort of advanced PD subjects. MethodsFifteen PD subjects were enrolled from three sites in a clinical trial that involved recordings from subthalamic nucleus (STN) deep brain stimulation (DBS) leads (NCT04620551). Video polysomnography (vPSG) data for a total of 45 nights were analyzed. Four experienced scorers independently scored data on initial review. Epochs with less than 75% consensus were flagged for secondary review. In secondary review of discordant epochs, two of the original scorers re-assessed epochs, from which the final consensus stage was derived. ResultsSleep stage classification agreement averaged 83.10% across all sleep stages on initial scoring (IS), and on secondary consensus scoring (CS) review, agreement reached 96.58%. Greatest disagreement was noted in determination of awake epochs (33.6% of discordant epochs) and non-rapid-eye-movement stage 2 (N2) epochs (31.8% of discordant epochs). Scoring discrepancy was resolved with direct measurement of cortical frequency and amplitudes, physiologic context of the epoch, and video review. ConclusionOur method of multi-level initial and then secondary consensus review scoring resulted in consensus scoring agreement superior to conventional standards. This work features a custom-engineered vPSG software and review platform for integration of consensus sleep stage scoring in a multi-site clinical trial.

Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings

Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1–4 Hz), theta (4–8 Hz), low-beta (8–20 Hz), high-beta (20–35 Hz), gamma (35–90 Hz), and high-frequency (200–400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.

Abnormal brain state distribution and network connectivity in a SYNGAP1 rat model.

Mutations in the SYNGAP1 gene are one of the common predictors of neurodevelopmental disorders, commonly resulting in individuals developing autism, intellectual disability, epilepsy, and sleep deficits. EEG recordings in neurodevelopmental disorders show potential to identify clinically translatable biomarkers to both diagnose and track the progress of novel therapeutic strategies, as well as providing insight into underlying pathological mechanisms. In a rat model of SYNGAP1 haploinsufficiency in which the exons encoding the calcium/lipid binding and GTPase-activating protein domains have been deleted (Syngap+/Δ-GAP ), we analysed the duration and occurrence of wake, non-rapid eye movement and rapid eye movement brain states during 6 h multi-electrode EEG recordings. We find that although Syngap+/Δ-GAP animals spend an equivalent percent time in wake and sleep states, they have an abnormal brain state distribution as the number of wake and non-rapid eye movement bouts are reduced and there is an increase in the average duration of both wake and non-rapid eye movement epochs. We perform connectivity analysis by calculating the average imaginary coherence between electrode pairs at varying distance thresholds during these states. In group averages from pairs of electrodes at short distances from each other, a clear reduction in connectivity during non-rapid eye movement is present between 11.5 Hz and 29.5 Hz, a frequency range that overlaps with sleep spindles, oscillatory phenomena thought to be important for normal brain function and memory consolidation. Sleep abnormalities were mostly uncorrelated to the electrophysiological signature of absence seizures, spike and wave discharges, as was the imaginary coherence deficit. Sleep spindles occurrence, amplitude, power and spread across multiple electrodes were not reduced in Syngap+/Δ-GAP rats, with only a small decrease in duration detected. Nonetheless, by analysing the dynamic imaginary coherence during sleep spindles, we found a reduction in high-connectivity instances between short-distance electrode pairs. Finally comparing the dynamic imaginary coherence during sleep spindles between individual electrode pairs, we identified a group of channels over the right somatosensory, association and visual cortices that have a significant reduction in connectivity during sleep spindles in mutant animals. This matched a significant reduction in connectivity during spindles when averaged regional comparisons were made. These data suggest that Syngap+/Δ-GAP rats have altered brain state dynamics and EEG connectivity, which may have clinical relevance for SYNGAP1 haploinsufficiency in humans.

0102 Performance Evaluation of a 24-hour Sleep-Wake State Classifier Derived from Research-Grade Actigraphy

Abstract Introduction Wrist-worn research-grade actigraphy devices are commonly used to identify sleep and wakefulness in freely-living people. However, common existing algorithms were developed primarily to classify sleep-wake within a defined in-bed period with PSG, and exhibit relatively high sensitivity (accuracy on sleep epochs) but relatively low specificity (accuracy on wake epochs). This classification imbalance results in the algorithms performing poorly when attempting to classify data that does not have a predefined sleep period, such as over a 24-hour interval. Here, we develop a 24-hour actigraphy classifier to overcome limitations in specificity (accuracy on wake epochs), which typically afflict in-bed focused algorithms. Methods Four datasets scored via either PSG or direct observation of daytime wakefulness were combined (n=52 participants of mean age 49.8yrs, age range 19 - 86; 52% male; 221 total days/nights). Actigraphy (counts) and PSG (RPSGT-staged epochs) were temporally aligned. A model was trained to transform a time-series actigraphy counts to a time series of sleep-wake classifications, using the TensorFlow library for Python. 5-fold cross-validation was used to train and evaluate the model. Classification performance was compared to the output of the Spectrum device (Philips-Respironics) using the Oakley algorithm with a wake threshold of ‘medium’. Results The developed classifier was compared to the Spectrum classifications across the 24-hour intervals. The developed classifier had higher accuracy (95.4% vs. 76.8%), higher specificity (95.9% vs. 68.9%) and higher balanced-accuracy (95.2% vs. 81.6%) relative to the Spectrum classifications, each assessed via paired-sample t-test. Sensitivity did not statistically differ (94.5% vs. 94.4%). Conclusion The model trained and evaluated on 24-hour data outperformed the existing algorithm output in terms of overall accuracy, specificity, and balanced accuracy, while sensitivity did not significantly differ. A model trained on 24-hour data may be more appropriate for analyses of freely living people, or older populations where napping is more common. Developing an accurate 24-hour sleep/wake classifier fosters new opportunities to evaluate sleep patterns in the absence of self-reports or assumptions about time in bed. Support (If Any) UL1TR002014, NSF#1622766, R43/44-AG056250

Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy

ObjectiveAnterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.MethodsWe analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities.ResultsWe found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).SignificanceThese results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

Discriminating Sleep From Freezing With Cortical Spindle Oscillations.

In-vivo longitudinal recordings require reliable means to automatically discriminate between distinct behavioral states, in particular between awake and sleep epochs. The typical approach is to use some measure of motor activity together with extracellular electrophysiological signals, namely the relative contribution of theta and delta frequency bands to the Local Field Potential (LFP). However, these bands can partially overlap with oscillations characterizing other behaviors such as the 4 Hz accompanying rodent freezing. Here, we first demonstrate how standard methods fail to discriminate between sleep and freezing in protocols where both behaviors are observed. Then, as an alternative, we propose to use the smoothed cortical spindle power to detect sleep epochs. Finally, we show the effectiveness of this method in discriminating between sleep and freezing in our recordings.

Exclusion of EEG-based arousals in wake epochs of polysomnography leads to underestimation of the arousal index.

There is an internal contradiction in current American Academy of Sleep Medicine standards for arousal index (AI) calculation in polysomnography: Arousals in sleep and wake epochs are counted, but only sleep time is used in the denominator. This study aimed to investigate the impact of including arousals scored in wake epochs on the AI. We compared AIs including (AIinc) vs excluding (AIexc) awake-epoch arousals from 100 consecutive polysomnograms conducted for investigation of possible obstructive sleep apnea. To determine the AI that most closely approximated "truth," AIinc and AIexc were compared to an AI calculated from continuous sleep analysis (AIcont) in a 20-polysomnogram subgroup of patients. The median (interquartile range) increase in AIinc was 5.2 events/h (3.5-8.1) vs AIexc (AIinc = 28.0 events/h [18.4-38.9] vs AIexc = 22.9 events/ h [13.1-31.3]), equating to an increase of 25.3% (15.6-40.8). As the AI increased, the difference increased (P < .001), with decreasing sleep efficiency and an increasing apnea-hypopnea index as the strongest predictors of the difference between AIexc and AIinc. The absolute AIexc-AIcont difference (7.7 events/h [5.1-13.6]) was significantly greater than the AIinc-AIcont difference (1.2 events/h [0.6-5.7]; z = -3.099; P = .002). There was a notable increase in AI when we included wake-epoch arousals, particularly in patients with more severe obstructive sleep apnea or reduced sleep efficiency. However, the AI including wake-epoch arousals best matched the "true" continuous sleep-scoring AI. Our study informs clinical and research practice, highlights epoch scoring pitfalls, and supports the current American Academy of Sleep Medicine standard arousal reporting approach for future standards. Wilson DL, Tolson J, Churchward TJ, Melehan K, O'Donoghue FJ, Ruehland WR. Exclusion of EEG-based arousals in wake epochs of polysomnography leads to underestimation of the arousal index. J Clin Sleep Med. 2022;18(5):1385-1393.

Non-contact Sleep/Wake Monitoring Using Impulse-Radio Ultrawideband Radar in Neonates.

Background: The gold standard for sleep monitoring, polysomnography (PSG), is too obtrusive and limited for practical use with tiny infants or in neonatal intensive care unit (NICU) settings. The ability of impulse-radio ultrawideband (IR-UWB) radar, a non-contact sensing technology, to assess vital signs and fine movement asymmetry in neonates was recently demonstrated. The purpose of this study was to investigate the possibility of quantitatively distinguishing and measuring sleep/wake states in neonates using IR-UWB radar and to compare its accuracy with behavioral observation-based sleep/wake analyses using video recordings.Methods: One preterm and three term neonates in the NICU were enrolled, and voluntary movements and vital signs were measured by radar at ages ranging from 2 to 27 days. Data from a video camcorder, amplitude-integrated electroencephalography (aEEG), and actigraphy were simultaneously recorded for reference. Radar signals were processed using a sleep/wake decision algorithm integrated with breathing signals and movement features.Results: The average recording time for the analysis was 13.0 (7.0–20.5) h across neonates. Compared with video analyses, the sleep/wake decision algorithm for neonates correctly classified 72.2% of sleep epochs and 80.6% of wake epochs and achieved a final Cohen's kappa coefficient of 0.49 (0.41–0.59) and an overall accuracy of 75.2%.Conclusions: IR-UWB radar can provide considerable accuracy regarding sleep/wake decisions in neonates, and although current performance is not yet sufficient, this study demonstrated the feasibility of its possible use in the NICU for the first time. This unobtrusive, non-contact radar technology is a promising method for monitoring sleep/wake states with vital signs in neonates.

O041 The impact of including oxygen desaturations occurring during awake epochs on the oxygen desaturation index

Abstract Introduction The oxygen desaturation index (ODI) is an important measure of sleep disordered breathing during polysomnography (PSG) however there is no accepted standard for its calculation. The AASM Manual for the Scoring of Sleep and Associated events (V2.6) does not specify whether oxygen desaturations occurring during awake epochs should be included. More generally, epoch-based scoring is potentially problematic for accurate ODI calculation. This study aims to compare the calculation of ODI including and excluding oxygen desaturations occurring during awake epochs and to determine the impact of sleep efficiency (SE) on any discrepancy. Methods Using twenty-one consecutive unattended PSG’s for investigation of OSA, two oxygen desaturation indices were calculated from each PSG; one excluding (ODIsleep) and one including (ODIall) oxygen desaturations marked in awake epochs. Results The median (IQR) ODIall was 19.3/h (10.3, 27.0) and ODIsleep was 13.0/h (6.6, 16.7). The median (IQR) difference (ODIall - ODIsleep) was 5.2/h (2.7, 10.4). This difference was greater with decreasing SE (r = -.63, p = .002). Patients with SE ≤ 75% (n=10) had a median ODI difference of 11.5/h (4.0, 17.6), and those with SE &amp;gt; 75% (n=11) had a difference of 2.8/h (2.0, 5.5) (p = .02). Discussion ODI was greater when including oxygen desaturations during awake epochs, with this discrepancy being greatest when SE is ≤ 75%. We plan to confirm these findings in a larger sample. This investigation informs clinical practice, highlights the difficulties of epoch scoring, and informs future standards for the scoring of sleep and associated events.

Validation of an EEG seizure detection paradigm optimized for clinical use in a chronically implanted subcutaneous device

BackgroundMany electroencephalography (EEG) based seizure detection paradigms have been developed and validated over the last two decades. The majority of clinical approaches use scalp or intracranial EEG electrodes. Scalp EEG is limited by patient discomfort and short duration of useful EEG signals. Intracranial EEG involves an invasive surgical procedure associated with significant risk making it unsuitable for widespread use as a practical clinical biometric. A less invasive EEG monitoring approach, that is between invasive intracranial procedures and noninvasive methods, would fill the need of a safe, accurate, chronic (ultra-long term) and objective seizure detection method. We present validation of a continuous EEG seizure detection paradigm using human single-channel EEG recordings from subcutaneously placed electrodes that could be used to fulfill this need. MethodsTen-minute long sleep, awake and ictal EEG epochs obtained from 21 human subjects with subscalp electrodes and validated against simultaneous iEEG recordings were analyzed by three experienced clinical neurophysiologists. The 201subscalp EEG time series epochs where classified as diagnostic for awake, asleep, or seizure by the clinicians who were blinded to all other information. Seventy of the epochs were classified in this way as representing seizure activity. A subject specific seizure detection algorithm was trained and then evaluated offline for each patient in the data set using the expert consensus classification as the gold standard. ResultsThe average seizure detection performance of the algorithm across 21 subjects exceeded 90 % accuracy: 97 % sensitivity, 91 % specificity, and 93 % accuracy. For 19 of 21 patient datasets the algorithm achieved 100 % sensitivity. For 15 of 21 patients, the algorithm achieved 100 % specificity. For 13 of 21 patients the algorithm achieved 100 % accuracy. ComparisonNo comparable published methods are available for subgaleal EEG seizure detection. ConclusionsThese findings suggest that a simple seizure detection algorithm using subcutaneous EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.

Examining Sex Differences in Response to NMDAR Antagonist MK‐801 on Sleep Architecture and Brain Function Using Electroencephalography in Rats

Accumulating evidence suggests glutamatergic hypofunction is one underlying factor in the pathology of schizophrenia. Administration of N-methyl-D-aspartate receptor (NMDAR) antagonists induces psychotomimetic-like symptoms in healthy humans and has emerged as a pharmacological model for symptoms of schizophrenia in animals. MK-801, an NMDAR antagonist, is frequently used to disrupt cognition and induce hyperlocomotion, modelling the cognitive and positive symptoms of schizophrenia, respectively. However, despite significant contributions of these models to surrounding literature, there are vast sex differences in the symptomology of schizophrenia in the human population that are underrepresented in animal models. Evidence suggests that prevalence and severity of negative and cognitive symptoms may be greater in males relative to females. Furthermore, sleep disturbances are underappreciated symptoms of schizophrenia, and sex differences in these have also been reported. Applying a translational biomarker approach that can be readily implemented in human subjects, present studies used polysomnography and quantitative electroencephalography (qEEG) measures to determine if NMDAR antagonists can reveal sex differences on sleep and brain function in animals similar to those reported in the human population. EEG surface electrodes and wireless transmitters were implanted in male and female Sprague-Dawley rats. EEG's were recorded for 24 hours in freely moving rats from their homecage. MK-801 (males: 0.056-0.56 mg/kg, sc; females: 0.03-0.3 mg/kg, sc) was administered 2 hours into the light cycle. Recordings were manually scored into wake, rapid eye movement (REM), and non-REM sleep, and qEEG spectral power was evaluated during waking epochs only. Activity counts were also simultaneously recorded. Consistent with previous literature, MK-801 induced hyperlocomotion, increased time awake, and decreased both NREM and REM sleep. Females were more sensitive than males to all of these measures, showing longer duration and magnitude of effects. Furthermore, MK-801 induced profound dose-dependent increases on both low and high gamma power in male rats. Interestingly, no comparable dose-dependent increases in gamma power were found in female rats across a full log unit dose range. Gamma power is highly sensitive to glutamate function, and abnormalities have been associated with cognitive impairment and psychotic symptoms in patients with schizophrenia. This is the first report, to our knowledge, of a blunted response to MK-801's effects in female rats compared to males despite increased sensitivity to all other reported measures. Ultimately, EEG revealed sex-related differences in NMDAR function which may correspond with prevalence/severity of symptoms of schizophrenia in humans. This may overall strengthen the validity of using NMDAR antagonism in preclinical model to experimentally induce schizophrenia-like symptoms.

Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

PurposeIn this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleepTM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB).MethodsPatients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0).ResultsWe found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen’s kappa of 0.62 was found for this 3-class classification problem.ConclusionThe algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.

An algorithm for actigraphy-based sleep/wake scoring: Comparison with polysomnography

ObjectiveTo evaluate the accuracy of actigraphy against polysomnography (PSG) as gold standard using a newly developed algorithm for sleep/wake discrimination that explicitly models the temporal structure of sleep. MethodsPSG was recorded in 11 men and 9 women (age 71.1±5.0) to evaluate suspected neuropsychiatric sleep disturbances. Simultaneously, wrist actigraphy was recorded, from which 37 features were computed for each 1-min epoch. We compared prediction of PSG-derived sleep/wake states for each of these features between our newly developed algorithm, and four state-of-the-art algorithms. The algorithms were evaluated using a leave-one-subject out cross validation. ResultsThe new algorithm classified 84.9% of sleep epochs (sensitivity) and 74.2% of wake epochs correctly (specificity), leading to a sleep/wake scoring accuracy of 79.0%. Four out of five sleep parameters were estimated more accurately by the new algorithm than by state-of-the-art algorithms. ConclusionThe proposed algorithm achieved a significantly higher specificity than state-of-the-art algorithm, with only minor decrease in sensitivity for patients with sleep disorders. We assume this reflects the capability of the algorithm to explicitly model sleep architecture. SignificanceThe unobtrusive assessment of sleep/wake cycles is particularly relevant for patients with neuropsychiatric diseases that are associated with sleep disturbances, such as depression or dementia.

1196 Machine Learning Derived-Interpretative Algorithm Better Differentiates Sleep and Wake Epochs and Estimates Sleep Parameters from Wrist Actigraphy Data

Abstract Introduction Several different interpretive algorithms (IAs) are available for scoring actigraphy-obtained body movement data for sleep and wake epochs. Although most have high sensitivity in detecting sleep epochs, they identify wake epochs poorly. We derived a machine learning (ML) based IA that improves differentiation of sleep and wake epoch to better estimate sleep parameters. Methods Forty-one adults (18 females) 26.6±12.0 years old underwent at-home single-night sleep assessment. Motionlogger® Micro Watch Actigraph recorded in zero crossing mode body movement per 30s epoch, with automated sleep scoring by single-channel electroencephalography (EEG) device (Zmachine® Insight+) as reference. The popular Cole-Kripke IA was applied to score body movement time series data of the following combination of current 1, preceding 4, and following 2 minute long epochs. Data of 21 subjects were utilized to train/derive the ML IA (logistic regression), and data of the other 20 subjects were used to test performance of it and the Cole-Kripke IA. Results In reference to the EEG, the Cole-Kripke actigraphy IA showed sensitivity of 0.98±0.02, specificity of 0.48±0.19, and kappa agreement of 0.53±0.16 in detecting sleep epochs, while the ML-derived IA showed corresponding values of 0.90±0.06, 0.71±0.14, and 0.57±0.11. The Cole-Kripke IA, relative to EEG, method significantly (P&amp;lt;0.05) underestimated sleep onset latency (SOL) by 18.0 min and wake after sleep onset (WASO) by 35.1 min, and overestimated total sleep time (TST) by 53.1 min and sleep efficiency (SE) by 9.6%. The ML-derived IA, relative to EEG significantly underestimated SOL by 15.1 min, but comparably (P&amp;gt;0.05) estimated WASO, TST, and SE. Conclusion The ML-derived IA, in comparison to Cole-Kripke IA, when applied to sleep-time wrist actigraphy data significantly better differentiates wake from sleep epochs and better estimates sleep parameters. Support This work was supported by the Robert and Prudie Leibrock Professorship in Engineering at the University of Texas at Austin.

0049 Odds Ratio Product as a Measure of Sleep Depth During REM Sleep: Effects on REM Duration and REM Sleep Fragmentation

Abstract Introduction The odds ratio product (ORP) is a new highly-validated electroencephalogram biomarker of sleep depth. ORP has been validated as such by several studies investigating the effect of sleep disorders, responses to sleep deprivation and traffic noise. ORP during REM sleep varies considerably among individuals. Whether ORP reflects sleep depth also in REM sleep is unknown. We hypothesized that subjects with high REM ORP are more prone to REM sleep fragmentation. Methods Using data from the baseline (SHHS1; N=5,537) and follow-up (SHHS2; N=2,595) visits of the Sleep Heart Health Study, we calculated and summarized ORP in 30-second intervals corresponding to manually scored sleep stage epochs. We developed a heuristic to identify REM periods, defined as sequences of REM sleep epochs separated by no more than 10 minutes of other sleep stages or wake epochs. Using general linear models adjusted by age, sex, body mass index, race and ethnicity, we evaluated the relationship between REM ORP and total REM duration, number of awakening episodes per REM period and arousal index during REM sleep. Results Higher REM ORP was correlated with shorter total REM duration (ρ SHHS1=-0.12; p &amp;lt 0.001, ρ SHHS2=-0.07; p &amp;lt 0.001), more awakening episodes (ρ SHHS1=0.26; p&amp;lt;0.001, ρ SHHS2=0.30; p &amp;lt 0.001) and higher arousal index (ρ SHHS1=0.18; p &amp;lt 0.001, ρ SHHS2=0.16; p &amp;lt &amp;lt; 0.001) during identified REM periods. In adjusted analyses, one-unit increase in REM ORP was associated, on average, with a 7 minute decrease in total REM duration (β=-7.10; p &amp;lt 0.001), 1 more awakening episode per REM period (β=1.29; p &amp;lt 0.001) and an increase of 6 arousals/hour (β=6.16; p &amp;lt 0.001) during REM sleep periods. Conclusion We found that higher REM ORP was associated with shorter REM periods, higher proportion of awake during REM periods and higher REM arousal index. Although small, these differences suggest that ORP is consistent with the concept of sleep depth also during REM sleep. Support None