PSG Recordings Research Articles

Clinical validation of a wireless patch-based polysomnography system.

Onera Health has developed the first wireless, patch-based, type-II PSG system, the Onera Sleep Test System (STS), to allow studies to be performed unattended at the patient's home or in any bed at a medical facility. The goal of this multicenter study was to validate data collected from the patch-based PSG to a traditional PSG for sleep staging and AHI. Simultaneous traditional PSG and patch-based PSG study data were obtained in a sleep laboratory from 206 participants with a suspected sleep disorder recruited from 7 clinical sites. Blinded, randomized scoring of the traditional PSG and patch-based PSG recordings was completed according to The AASM Manual for the Scoring of Sleep and Associated Events, version 2.6 criteria by three independent scorers. Concordance correlation coefficients were high between the patch-based device and traditional PSG across essential sleep and respiratory variables - TST (0.87); Wake (0.84); NREM (0.80); N1 (0.72); N2 (0.71); N3 (0.64); REM (0.80) and AHI (0.94). There was substantial agreement between epoch sleep staging scored on the patch-based device and traditional PSG (average Cohen's kappa of 0.62 ± 0.13 across all scorers). The patch-based type-II PSG had a similar performance on sleep staging and respiratory variables when compared to Traditional PSG, thus making it possible to use the patch-based PSG for a routine PSG study. These results open the possibility of performing unattended PSG studies efficiently and accurately outside the sleep laboratory improving access to high quality sleep assessments for patients with sleep disorders. Registry: ClinicalTrials.gov; Identifier: NCT05310708.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability

Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instruments, montage of recording, subject type, and scoring manual factors. This study aimed to develop a deep learning model that addresses generalization problems by integrating enzyme-inspired specificity and employing separating training approaches. Subject type and scoring manual factors were controlled, while the focus was on instruments and montage of recording factors. The proposed model consists of three sets of signal-specific models including EEG-, EOG-, and EMG-specific model. The EEG-specific models further include three sets of channel-specific models. All signal-specific and channel-specific models were established with data manipulation and weighted loss strategies, resulting in three sets of data manipulation models and class-specific models, respectively. These models were CNNs. Additionally, BiLSTM models were applied to EEG- and EOG-specific models to obtain temporal information. Finally, classification task for sleep stage was handled by ‘the-last-dense’ layer. The optimal sampling frequency for each physiological signal was identified and used during the training process. The proposed model was trained on MGH dataset and evaluated using both within dataset and cross-dataset. For MGH dataset, overall accuracy of 81.05 %, MF1 of 79.05 %, Kappa of 0.7408, and per-class F1-scores: W (84.98 %), N1 (58.06 %), N2 (84.82 %), N3 (79.20 %), and REM (88.17 %) can be achieved. Performances on cross-datasets are as follows: SHHS1 200 records reached 79.54 %, 70.56 %, and 0.7078; SHHS2 200 records achieved 76.77 %, 66.30 %, and 0.6632; Sleep-EDF 153 records gained 78.52 %, 72.13 %, and 0.7031; and BCI-MU (local dataset) 94 records achieved 83.57 %, 82.17 %, and 0.7769 for overall accuracy, MF1, and Kappa respectively. Additionally, the proposed model has approximately 9.3 M trainable parameters and takes around 26 s to process one PSG record. The results indicate that the proposed model demonstrates generalizability in sleep stage classification and shows potential as a feasibility tool for real-world applications. Additionally, enzyme-inspired specificity effectively addresses the challenges posed by varying montage of recording, while the identified optimal frequencies mitigate instrument-related issues.

Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, apnea and periodic leg movement

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz_Cz, Cz_Oz, and C4_M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea.

Automatic Wake and Deep-Sleep Stage Classification Based on Wigner-Ville Distribution Using a Single Electroencephalogram Signal.

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner-Ville Distribution (WVD) for time-frequency analysis to determine EEG energy per second in specific frequency bands (δ, θ, α, and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10-20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future.

Is there an association between sleep bruxism and obstructive sleep apnea? A case-control polysomnographic investigation

ObjectivesTo estimate the statistical and epidemiological association between Sleep bruxism (SB) and Obstructive sleep apnea (OSA) based on OSA severity, and to describe sleep data findings within the analyzed population. MethodsA case-control study (N = 37) was conducted on subjects with and without OSA. All subjects underwent a full-night polysomnographic recording at the Sleep Unit (Clinical Neurophysiology Department) of San Carlos University Hospital. The diagnosis and severity of OSA were determined using ICSD-3 and AASM-2.6 scoring. The definitive SB diagnosis was obtained through a self-report test, physical examination, and PSG recordings. Variables used to study the association between both conditions included the apnea and hypopnea episodes, the Apnea-hypopnea index (AHI), the number of SB episodes per night, and the bruxism index. Chi2, correlations, and ANOVA were calculated. The epidemiological association was calculated using the OR. ResultsSB showed an epidemiological association with OSA, with an OR of 0.15 (0.036-0.68), suggesting it could be considered a protective factor (p < 0.05). OSA patients presented fewer average SB episodes (6.8 ± 12.31) than non-OSA patients (25.08 ± 31.68). SB episodes correlated negatively (p < 0.05) with the AHI and the number of hypopneas (p < 0.05). The average number of SB episodes was significantly higher in patients with mild OSA compared to those with severe OSA. ConclusionsIn this sample of patients with subclinical and mild OSA, SB may act as a protective factor. However, confirmation of these results with a larger sample size is necessary.

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals.

Sleep staging is crucial for assessing sleep quality and diagnosing sleep disorders. Recent advances in deep learning methods with electroencephalogram (EEG) signals have shown remarkable success in automatic sleep staging. However, the use of deeper neural networks may lead to the issues of gradient disappearance and explosion, while the non-stationary nature and low signal-to-noise ratio of EEG signals can negatively impact feature representation. To overcome these challenges, we proposed a novel lightweight sequence-to-sequence deep learning model, 1D-ResNet-SE-LSTM, to classify sleep stages into five classes using single-channel raw EEG signals. Our proposed model consists of two main components: a one-dimensional residual convolutional neural network with a squeeze-and-excitation module to extract and reweight features from EEG signals, and a long short-term memory network to capture the transition rules among sleep stages. In addition, we applied the weighted cross-entropy loss function to alleviate the class imbalance problem. We evaluated the performance of our model on two publicly available datasets; Sleep-EDF Expanded consists of 153 overnight PSG recordings collected from 78 healthy subjects and ISRUC-Sleep includes 100 PSG recordings collected from 100 subjects diagnosed with various sleep disorders, and obtained an overall accuracy rate of 86.39% and 81.97%, respectively, along with corresponding macro average F1-scores of 81.95% and 79.94%. Our model outperforms existing sleep staging models in terms of overall performance metrics and per-class F1-scores for several sleep stages, particularly for the N1 stage, where it achieves F1-scores of 59.00% and 55.53%. The kappa coefficient is 0.812 and 0.766 for the Sleep-EDF Expanded and ISRUC-Sleep datasets, respectively, indicating strong agreement with certified sleep experts. We also investigated the effect of different weight coefficient combinations and sequence lengths of EEG epochs used as input to the model on its performance. Furthermore, the ablation study was conducted to evaluate the contribution of each component to the model's performance. The results demonstrate the effectiveness and robustness of the proposed model in classifying sleep stages, and highlights its potential to reduce human clinicians' workload, making sleep assessment and diagnosis more effective. However, the proposed model is subject to several limitations. Firstly, the model is a sequence-to-sequence network, which requires input sequences of EEG epochs. Secondly, the weight coefficients in the loss function could be further optimized to balance the classification performance of each sleep stage. Finally, apart from the channel attention mechanism, incorporating more advanced attention mechanisms could enhance the model's effectiveness.

Sleep architecture and Nusinersen therapy in children with Spinal Muscular Atrophy type 1

BackgroundSpinal muscular atrophy (SMA) is a severe neuromuscular disorder, the phenotype of the disease is caused by the mutation of the SMN1 (survival motor neuron 1) gene which encodes for the SMN protein. Innovative treatments for SMA have become available and the first molecule approved is Nusinersen, an antisense oligonucleotide that increases the production of SMN protein. Nusinersen has been shown to be associated with a significant motor improvement and an increase of the event‐free survival. For these reasons the aim of the present study is to assess if Nusinersen is able modify sleep architecture and microstructure and to improve sleep structure in these patients. MethodsSixteen patients affected by SMA1 were enrolled in the study (4 boys, 12 girls; median age 72.5 months, intelligence quotient range 24–84). All patients underwent complete nocturnal PSG before the start of the treatment trough intrathecal injections with Nusinersen (T0) and after the fifth infusion (day 180, T180). PSG recordings were visually scored and interpreted according to the indications of the American Academy of Sleep Medicine (AASM) and and microstructure by means of the Cyclic Alternating Pattern (CAP). ResultsAfter 6 months therapy we found a significantly reduced sleep latency and a significantly increased sleep efficiency. Regarding sleep microstructure parameters (CAP), we did not find any significant change after therapy however, it is worth mentioning that a moderate effect size was observed for the increase in CAP A3 index. ConclusionsWe observed short-term effects of Nusinersen on sleep with an improvement in sleep efficiency and reduction in sleep onset latency; regarding sleep microstructure, a moderate effect size was found for the number of CAP A3 subtypes that slightly increased, possibly indicating a slightly higher arousability. This finding points at a probably overall better sleep pattern organization associated with the treatment, but they need to be confirmed by larger studies with patients treated earlier in life and for a longer period.

0409 Is insomnia with objective short sleep duration related to insufficient sleep drive or physiological hyperarousal?

Abstract Introduction IInsomnia with objective short sleep duration (OSSD) was found to be associated with higher risks of cardiometabolic disorders, and was therefore proposed to be a phenotype associated with higher physiological hyperarousal. Previous studies further reported that insomnia with OSSD had higher autonomic arousal as measured by heart rate variability (HRV) during sleep. Since sleep duration could be determined by the interplay of arousal and sleep drive and HRV during sleep could be influenced by sleep staging and quality, the present study examined the level of arousal and sleep drive prior to sleep with EEG power and HRV to further understand the phenotyping of insomnia. Methods Ninety-four insomnia patients participated in the study and went through one night of 8-hour PSG recording. They were divided, with a cut-off of PSG total sleep time of 6 hours, into two groups: an OSSD group (N=26; mean age=37.0 ±10.3; M:F=7:19; TST=286.21±87.21 min) and an objective normal sleep duration (ONSD) group (N=68, mean age=32.95 ±10.05, M:F=12: 56, TST=414.4±28.9 min). Resting-state EEG and EKG were recorded for a 6-min period, 3-min eye-opened (EO) with fixation focus and 3 mins eye-closed (EC), prior to bedtime. Results T-tests showed no significant difference in HRV measures (SDNN, RMSD, LF, HF, LF/HF) between the two groups, but shorter R-R interval for OSSD group than ONSD group. Mann-Whitney tests for EEG power showed significantly lower theta power at F3, F4, C3, C4, and O2, and higher sigma power at F3, C3, C4, O1, and O2 during EC, as well as lower theta power at C3 and higher sigma power at O2 during EO for OSSD group in comparison to ONSD group. Conclusion The results partially support that insomnia with OSSD is associated with physiological hyperarousal as indicated by faster heart rate and higher high frequency EEG power prior to sleep. However, the lower EEG theta power prior to sleep suggest that lower homeostatic sleep drive may also play a role for the insomnia with short objective sleep duration. Support (if any) Supported by Taiwan National Science and Technology Council

0274 Effect of aging on sleep architecture including a novel REM Behavior Disorder phenotype in the PS19 mouse model of tauopathy and effect of a dual orexin receptor antagonist

Abstract Introduction This project examines changes to sleep micro-architecture with aging in the PS19 (MAPT P301S) model of tauopathy and response to a dual orexin receptor antagonist (DORA-12) in aged PS19 mice. Methods 24-hour video PSG recordings occurred in 28 PS19 mice and 22 littermate controls at 2-3 months (young) and 10-14 months (old). Results Spindle density significantly deceased as a function of both advanced age and PS19 genotype without interaction. We observed a significant interaction between age and genotype on slow oscillation (SO) density such that SO density was higher in young PS19 lower in old PS19 mice vs controls without main effects of age or genotype. Phase amplitude coupling of spindles to detected SO events was significantly decreased as a function of age with a trend toward an age/genotype interaction such that the reduction in coupling was greater with aging in PS19 mice vs controls. We observed unexpected dream enactment during REM in 3 of 11 old PS19 but not in young PS19 mice or control mice at any age. Normalized cumulative EMG area during REM sleep, cumulative duration of elevated EMG in REM, and %REM w/o atonia were all significantly increased in mice displaying dream enactment.Old PS19 mice were also video-PSG recorded for 24 hours while receiving 100 mg/kg DORA-12 vs vehicle control orally twice daily. DORA-12 resulted in significant reduction in latency to persistent NREM and REM sleep, and significant increases in spindle density and SO density, without significant difference in spindle-SO coupling. While we did not observe significant differences in dream enactment measures with DORA-12 due to low sample size, all 3 mice with dream enactment displayed decreases in normalized EMG amplitude in REM and %REM with EMG bursts with DORA-12 ranging from 10 to 60%. Conclusion Given the importance of spindles, SO’s, and their coupling on cognitive processes, these observations can motivate further evaluation of DORA’s toward such cognitive processes in neurodegenerative models as well as effect of DORA’s on RBD phenotypes. Support (If Any)

Electroencephalography Theta/Beta Ratio Decreases in Patients with Severe Obstructive Sleep Apnea.

PurposeAccumulating evidence suggests that theta/beta ratio (TBR), an electroencephalographic (EEG) frequency band parameter, might serve as an objective marker of executive cognitive control in healthy adults. Obstructive sleep apnea (OSA) has a detrimental impact on patients’ behavior and cognitive performance while whether TBR is different in OSA population has not been reported. This study aimed to explore the difference in relative EEG spectral power and TBR during sleep between patients with severe OSA and non-OSA groups.Patients and Methods142 participants with in-laboratory nocturnal PSG recording were included, among which 100 participants suffered severe OSA (apnea hypopnea index, AHI > 30 events/hour; OSA group) and 42 participants had no OSA (AHI ≤ 5 events/h; control group). The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The demographic and polysomnographic characteristics, relative EEG spectral power and TBR of the two groups were compared.ResultsIt was found that the beta band power during NREM sleep and total sleep was significantly higher in the OSA group than controls (p < 0.001, p = 0.012, respectively), and the theta band power during NREM sleep and total sleep was significantly lower in the OSA group than controls (p = 0.019, p = 0.014, respectively). TBR during NREM sleep, REM sleep and total sleep was significantly lower in the OSA group compared to the control group (p < 0.001 for NREM sleep and total sleep, p = 0.015 for REM sleep). TBR was negatively correlated with AHI during NREM sleep (r=−0.324, p < 0.001) and total sleep (r=−0. 312, p < 0.001).ConclusionTBR was significantly decreased in severe OSA patients compared to the controls, which was attributed to both increased beta power and decreased theta power. TBR may be a stable EEG-biomarker of OSA patients, which may accurately and reliably identify phenotype of patients.

A series of 8 cases of sleep-related psychogenic dissociative disorders and proposed updated diagnostic criteria.

To identify the most relevant clinical and video-polysomnographic characteristics of patients with sleep-related dissociative disorders (SRDDs) and to propose a framework for new diagnostic criteria. We searched potential SRDD cases from the scientific literature and from a database of patients referred for clinical and video-polysomnographic assessment in a single sleep disorders center for disruptive nocturnal behaviors (n = 731). The most relevant clinical and neurophysiological characteristics of the cases were extracted and a descriptive analysis was performed. Twenty-six SRDD cases (8 new and 18 previously published cases) were reviewed. Almost all cases of SRDDs occurred in a context of past traumatic events or abuse and were associated with at least 1 comorbid psychiatric disorder. We highlighted 4 relevant clinical characteristics of SRDD useful for the differential diagnosis with parasomnias: episodes of long duration of more than 1 hour (90.9%), self-inflicted injuries (83.3%), occurrence while awake close to bedtime (35.7%), and the presence of daytime dissociative symptoms (72.7%). The video-polysomnography documented typical episodes of SRDD with prolonged wakefulness before, during, and after the event in 11/26 cases. New diagnostic criteria for SRDD were proposed, with 3 levels of certainty for the diagnosis based on clinical, video-polysomnographic, and homemade video findings. More than 30 years after its formal identification, SRDD is not currently recognized as an official diagnostic entity. We better delineate the clinical and neurophysiological features of SRDD and propose a framework for its reinstatement in the next revisions of the sleep and psychiatric disorders classifications. Lopez R, Lefevre L, Barateau L, Micoulaud-Franchi J-A, Dauvilliers Y, Schenck CH. A series of 8 cases of sleep-related psychogenic dissociative disorders and proposed updated diagnostic criteria. J Clin Sleep Med. 2022;18(2):563-573.

Increased chin muscle tone during all sleep stages in children taking selective serotonin reuptake inhibitor antidepressants and in children with narcolepsy type 1.

To assess if selective serotonin reuptake inhibitor (SSRI) antidepressants are able to modify the chin electromyogram (EMG) tone during sleep also in children. Twenty-three children and adolescents (12 girls, mean age 14.1 years, SD 2.94) under therapy with antidepressant for their mood disorder were consecutively recruited and had a PSG recording. Twenty-one were taking were taking SSRI and treatment duration was 2-12 months. An age- and sex matched group of 33 control children (17 girls, mean age 14.2 years, SD 2.83) and 24 children with narcolepsy type 1 (12 girls, mean age 13.7 years, SD 2.80) were also included. The Atonia Index was then computed for each NREM sleep stage and for REM sleep, also all EMG activations were counted. Atonia Index in all sleep stages was found to be significantly reduced in children with narcolepsy followed by the group taking SSRI antidepressants and the number of EMG activations was also increased in both groups. Fluoxetine, in particular, was found to be significantly associated with reduced Atonia index during NREM sleep stages N1, N2, and N3, and with an increased number of EMG activations/hour during sleep stage N3. Similarly to adults, SSRI antidepressants are able to modify the chin EMG tone in children during REM sleep, as well as during NREM sleep stages. Different pharmacological properties of the different SSRI might explain the differential effect on chin tone during sleep found in this study.

Risk assessment of obstructive sleep apnea syndrome and other sleep disorders in multiple sclerosis patients

BackgroundThe aim of the present study was to determine the possible risk of OSAS in patients with MS through the STOP-BANG questionnaire, and to confirm the pre-diagnosis of OSAS by recording polysomnographic investigation in individuals with high risk. In addition, the relationship between OSAS risk and fatigue, sleepiness, depression, and disability status will be examined. MethodsTotally 97 patients with multiple sclerosis including 36 males and 61 females with an age average of 39.92 ± 9.11 years. All participants completed the following questionnaires: STOP-Bang, Fatigue Severity Scale (FSS), Epworth sleepiness scale (ESS), Beck Depression Inventory (BDI); disability status of the participants was assessed by Expanded Disability Status Scale (EDSS). Polysomnographic sleep record was applied to the patients with high risk of OSAS according to STOP-BANG test scores. ResultsThe STOP_BANG questionnaire revealed that 24.7% of the patients were screened as high risk for OSA. Approximately 11.3% of the patients were detected positive for OSAS based on PSG recording. Comparison of MS patients with high risk of OSA with others suggested a significant difference in terms of the age (p = 0.01). ESS positive scores were significantly correlated with positive STOP BANG outcomes (p < 0.001). ESS positive scores were negatively correlated with positive PSG outcomes. ConclusionThe prevalence of OSAS in MS patients based on questionnaire and PSG was found consistent with literature. Similar to the general population, increasing age was found as a risk factor for OSAS in patients with MS. STOP-BANG test may not be an adequate test to diagnose OSAS, especially in MS patients with high fatigue scores.

217 Sleep in heavy marijuana users after smoking differing THC doses compared to controls

Abstract Introduction Sleep disturbances are commonly reported by chronic marijuana (MJ) users and often identified as reasons for MJ relapse and/or other drug use. In the current study we compared the sleep architecture of 12 heavy MJ users to 11 normal controls. Methods Participants in the marijuana group met DSM-V criteria for cannabis use disorder but were otherwise healthy individuals. On the first study day, individuals smoked (1330-1400 hr) 11 puffs from a cannabis cigarette (7% THC). During the next four days, under varying experimental contingencies participants smoked an average of 4.58 (±3.48) day 1, 4.92 (±3.62) day 2, 4.75 (±3.52) day 3, and 4.17 (±3.56) day 4 puffs from cannabis cigarettes (7% THC). Their sleep was recorded the first four study nights using standard polysomnography procedures at Henry Ford Sleep and Research Center Hospital, under an 8-hr fixed time in bed (2300-0700 hr). Controls (n=11) had no history of illicit drug use or medical illness and were not shift workers. Neither group reported a history of sleep-related disorders. PSG recordings were scored using Rechtschaffen and Kales standard criteria. Sleep measures included sleep efficiency (total sleep time/time in bed * 100), latency to persistent sleep, and percent of time spent in Stage 1, 2, 3/4, and rapid eye movement (REM). Results PSGs taken across all four nights of inpatient stay showed that MJ users spent significantly more time in REM sleep compared to controls (means 24.91, 24.64, 24.42, 24.13 vs 18.81, p&amp;lt;.001) and less time in stage 3/4 sleep (means 4.33, 4.79, 4.53, 6.91 vs 15.68, p&amp;lt;.001). MJ users showed reduced sleep efficiency compared to controls on night 4 (means 82.03 vs 90.32, p=0.039), and increased latency to persistent sleep on night 1 (means 6.04 vs 17.77, p=0.026). Conclusion These data show reduced sleep efficiency, lightened sleep (reduced stage 3/4), as well as an increased duration during REM sleep in heavy MJ users during decreased use, findings that are predictive of relapse in other drug abuse populations. Support (if any) NIH/NIDA R21 DA040770 (LHL)

325 Stress-Induced Physiological Profiles Among Different Phenotype Insomniacs

Abstract Introduction Stress reactivity and autonomic nervous system (ANS) dysregulation have been suggested to be the pathophysiology of insomnia. Based on the finding PSG-measured short sleep duration was associated with higher morbidity of metabolic and cardiovascular disease. Vgontzas and Fernandez-Mandoza (2013) proposed that objective sleep duration is a biomarker for insomnia phenotypes. The phenotype with short objective sleep duration is associated with increased stress-related physiological hyperarousal. The present study aims to test this hypothesis by comparing the stress-induced cardiovascular reactivity between insomnia patients with short and long objective sleep durations. Methods 27 insomnia patients (age mean 34.48 ±12.87, Male: Female= 6:21) without comorbidity of psychiatric, medical or sleep disorders participated in this study. They went through one night of 8-hour PSG recording and were divided into two groups by their total sleep time with a cutoff of 6 hours. Nine participants were in short sleep duration group and 18 in longer sleep duration group. Psychophysiological reactivity profile, as recorded with EKG, skin conductance (SC), body temperature (BT), blood volume pulse (BVP), respiration rate (RR), was measured under three conditions: baseline resting state, arithmetic word problems solving, and recovery resting state. Results Both groups showed similar stress physiological response with increased heart rate (HR) and SC, nearly equivalent BT and BVP, and decreased RR when solving arithmetic problems, and opposite reaction during recovery resting state. Mann-Whitney U test comparing the changes from baseline resting state on all the psychophysiological measures between two phenotypes of insomnia showed no significant differences: stress-induced heart-rate (U=106, p=.119.) recovery heart-rate (U=44, p=.095), stress-induced skin conductance (U=104.5, p=.132),recovery skin conductance (U=51.5, p=.198), stress-induced body temperature (U=79, p=.897),recovery body temperature (U=60.5, p=.418), stress-induced blood volume pulse amplitude (U=77, p=1.0), and recovery blood volume pulse amplitude (U=69, p=.735), stress-induced respiration rate (U=76, p =.696), and recovery respiration rate (U=85, p=.658). Conclusion Our results indicate that the insomnia phenotypes with short and long objective sleep duration are not different in their stress-induced physiological responses. Future studies are needed to confirm these results and to explore other mechanisms for the increased metabolic and cardiovascular disease risk in insomnia patients with short objective sleep duration. Support (if any):

Can we enhance compliance to treatment by performing a continuous positive airway pressure trial in obstructive sleep apnea?

Although continuous positive airway pressure (CPAP) therapy is the most effective treatment for obstructive sleep apnea (OSA), it is not always easy to gain adherence to therapy. We aimed to evaluate how short-term CPAP application during the daytime before the titration night affects polysomnographic data and CPAP adherence in OSA. Patients with moderate to severe OSA for whom CPAP titration was recommended were prospectively randomized to daytime CPAP application (group 1) or usual care (group 2). For group 1,CPAP was applied for 30-60 min in daytime conditions to acclimate patients to the device. An appointment was thenmade to perform CPAP titration with PSG. In group2 (usual care), the first CPAP application was performed on the titration night. PSG recordings and titration night recordings of both groups were compared. All subjects were evaluated 1 month after the initiation of CPAP treatment. Among 246 cases, first night data were similar in both groups. During the titration night, total sleep time, sleep efficiency, andtime in stage N3 were significantly higher in patients who underwent the daytimeCPAP trial. Adherence to CPAP treatment at first-month follow-up was significantly higher in the group1 (5.7 ± 1.0 h/night) compared to the group 2(3.9 ± 1.1 h/night, p < 0.001). A short-term daytimeCPAP trial in patients before the titration nightmay provide longer and more efficient sleep on the titration night and better CPAP adherenceat one month.

Home-Use and Real-Time Sleep-Staging System Based on Eye Masks and Mobile Devices with a Deep Learning Model

PurposeSleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID‑19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper.MethodsWe developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing.ResultsThe averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements.ConclusionThese experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.

THE UTILITY OF POLYSOMNOGRAPHY(PSG) MACHINE AS A RELIABLE LIE DETECTOR IN AN EXPERIMENTALLY INDUCED MOCK THEFT SITUATION

Objective- To compare the various parameters in polysomnographic recordings of individuals who had indulged in experimentally induced theft situation with subjects who had not indulged in an experimentally induced theft situation. Material and methods-The present study was conducted in the department of psychiatry, Sarojini Naidu Medical College (S.N.M.C.) Agra, between the periods, March 2010 and September 2011. The sample consisted of 102 medical students who had volunteered to participate in the study. These volunteers were selected on the following criteria's. Result- The present study finding shows statistically significant difference on EEG amplitude, there are C3-A2 lead EEG amplitude were significant in group A and group C (control versus stolen, at baseline p-value = 0.0001; after 10th minute p-value = 0.0007; and after 20th minute p-value = 0.0129); EEG amplitude in O1-A2 lead were significant in control and stolen group ( at baseline p-value = 0.0019 and after 10th minute p-value = 0.0505); EEG amplitude in O2-A1 lead were found significant in control versus stolen group ( at baseline p-value = 0.0002, after 10th minute p-value = 0.0347, and 20th minute p-value = 0.0262). Conclusion- The changes observed in the amplitude of C3-A2 and O2-A1 leads along with C4-A1 (mv), O1-A2 (mv) leads could better identify the subjects who had stolen money, due to higher apprehension created for being caught during their PSG recordings while other leads which did not show significant changes in the PSG recordings could be considered as being not affected during deception. Keyword- Polysomnography, Amplitude, Lie Detection

CONTINUOUS NOCTURNAL BP MONITORING DURING POLYSOMNOGRAPHY: PROOF OF CONCEPT STUDY

SESSION TITLE: Sleep Disorders Posters SESSION TYPE: Original Investigation Posters PRESENTED ON: October 18-21, 2020 PURPOSE: Obstructive sleep apnea (OSA) is associated with cardiovascular disease, but recent randomized trials showed that current diagnostic and therapeutic algorithms for treating OSA do not lead to ideal improvements in cardiovascular outcomes. Nocturnal blood pressure (BP) is a strong predictor of adverse cardiac events, yet nocturnal BP is an under-explored modality in assessing cardiovascular impacts of OSA in part because previous BP technologies were interruptive to sleep and difficult to integrate with multi-channel PSG. METHODS: We set to develop an expanded PSG set-up which would: 1) enable a continuous BP recording without interrupting sleep, 2) allow integration of the BP channel into the existing multi-lead PSG, and 3) record accurate BP measurements as compared with traditional brachial cuff-based nocturnal BP measurements. To achieve these aims, we modified a non-invasive finger-based continuous hemodynamic monitor (plethysmograph and finger-based BP cuff system) to complement an existing attended in-laboratory PSG. This novel expanded PSG set-up was tested on a healthy sleeping subject. RESULTS: In this proof-of-concept study performed during sleep we showed that our novel expanded PSG set-up: 1) continuously recorded BP without interrupting sleep; 2) modifications to the existing non-invasive hemodynamic monitor allowed for integration into existing PSG recording system with direct display of continuous BP on the PSG monitor screens (Figure); 3) the accuracy of the continuous finger-based BP measurement was similar to the traditional BP obtained from brachial cuff [mean BP difference = 1.8 +/- 4.9 mmHg]. CONCLUSIONS: Continuous nocturnal non-invasive BP recording is feasible and could be cost-effectively added to presently used PSG laboratory set-up. Finger based BP recording accurately corresponds to brachial BP, and it does not interrupt sleep or cause excessive arousals. CLINICAL IMPLICATIONS: Our methodology of continuous nocturnal BP recording can be effectively used in subsequent clinical studies. This novel set-up would allow for routine assessment of OSA-induced nocturnal BP changes, and it would allow for BP directed titration of OSA therapies. DISCLOSURES: No relevant relationships by Richard Castriotta, source=Web Response No relevant relationships by Alexander Gong, source=Web Response Speaker/Speaker's Bureau relationship with Respicardia Please note: $1001 - $5000 Added 06/22/2020 by Vincent Grbach, source=Web Response, value=Honoraria No relevant relationships by Howard Hodis, source=Web Response No relevant relationships by Michael Khoo, source=Web Response No relevant relationships by Tomas Konecny, source=Web Response No relevant relationships by Andrew Lee, source=Web Response No relevant relationships by Arash Motamed, source=Web Response No relevant relationships by Winston Tran, source=Web Response no disclosure on file for Edwin Valladares

Effects of new PLM scoring rules on PLM rate in relation to sleep and resting wake for RLS and healthy controls.

This study evaluates the differences in periodic leg movement (PLM) rates for Restless Legs Syndrome (RLS) and healthy controls when using the updated PLM scoring criteria developed by IRLSSG in 2016 versus the prior PLM scoring criteria developed by IRLSSG in 2006. Four major problems with the prior standards had been objectively identified, i.e. minimum inter-movement interval should be 10 not 5s, non-PLM leg movements should end any preceding PLM sequence, a leg movement (LM) can be any length > 0.5s, and a PLM should be a persisting movement not a couple or a series of closely spaced, very brief events. Each of these led to including, erroneously, various random leg movements as PLM. Correcting these problems was expected to increase specificity, reducing the number of PLM detected, particularly in situations producing relatively more random leg movements, e.g. wake vs. sleep and controls without PLMD vs. RLS patients. This study evaluated the putative benefits of the updated, 2016-scoring criteria. The LMs from 42 RLS patients and 30 age- and gender-matched controls were scored for PLMS and PLMW from standard all-night PSG recordings using both 2006 and 2016 WASM criteria. The results confirmed that that the 2016 compared to the 2006 criteria generally decreased the PLM rates with particularly large decreases for the conditions with more random non-PLM events, e.g. wake times and normal healthy controls. This supported the view that the new criteria succeeded in increasing the specificity of PLM detection. Moreover, the changes in PLM rates were generally small for the conditions with relatively few random LM, e.g. RLS and sleep. Thus the bulk of existing PLMS research does not require reconsideration of results, with possible exception of special situations with relatively more random leg movements than periodic leg movements, e.g. wake, healthy normals and children.