Characteristics and reproducibility of novel sleep EEG biomarkers and their variation with sleep apnea and insomnia in a large community-based cohort

Multivariate deep phenotyping reveals behavioral correlates of non-restorative sleep in 22q11.2 deletion syndrome

Converging evidence suggests that sleep disturbances can directly contribute to a transdiagnostic combination of behavior and neurocognitive difficulties characterizing most forms of psychopathology. However, it remains unclear how the growing comprehension of sleep neurophysiology should best inform sleep quality assessment in mental health patients. To address this fundamental question, we performed deep multimodal sleep and behavioral phenotyping in 37 individuals at high genetic risk for psychopathology due to 22q11.2 Deletion Syndrome (Mean age:19±8.17, M/F = 22/15) and 34 Healthy Controls (Mean age:17.06±6.87, M/F = 12/22). We implemented a multivariate analysis pipeline informed by the current neurobiological understanding of the behavioral consequences of sleep disruption. We detected multivariate patterns of disrupted sleep architecture consistently influenced by age and diagnosis across recordings and experimental settings. With high-density EEG polysomnography we detected atypical trajectories of Slow-Wave-Activity (SWA) reduction, influenced by age and sleep duration which, according to the Synaptic-Homeostasis-Hypothesis, could reflect combined alterations in neurodevelopmental and synaptic homeostasis mechanisms in 22q11DS. Blunted SWA reduction was linked with EEG markers of residual sleep pressure in morning-vs-evening EEG and with questionnaires estimating subjective somnolence in everyday life, potentially representing a clinically relevant signature of non-restorative sleep. Moreover, blunted SWA decline was linked to a transdiagnostic combination of behavioral difficulties, including negative psychotic symptoms, ADHD symptoms, and neurocognitive impairments in processing speed and inhibitory-control. These findings suggest that systematic screening and management of sleep disturbances could directly improve behavioral outcomes in 22q11DS. They highlight the potential of precision/multivariate phenotyping approaches for characterizing the role of sleep disturbances in developmental psychopathology.

Ordinal Sleep Depth: A Data-Driven Continuous Measurement of Sleep Depth

Conventional sleep staging categorises sleep into discrete stages, which may not capture the continuous nature of sleep depth. We aimed to develop a data-driven continuous measure of sleep depth-ordinal sleep depth (OSD)-using a deep learning framework, and to evaluate its correlation with arousal probability and its association with age, sex, sleep-disordered breathing (SDB) and cognitive impairment. We used 21,787 polysomnography recordings from 18,116 unique patients. A convolutional neural network was trained on 3-s EEG segments to estimate sleep depth continuously, incorporating ordinal regression for the ordered nature of non-REM stages. OSD was compared with the odds ratio product (ORP). Correlations with sleep stages, Arousal Index and clinical variables were assessed. OSD showed a strong linear correlation with arousal probability (Pearson's r = 0.994), slightly outperforming ORP (r = 0.923). Both OSD and ORP reflected expected decreases in sleep depth with advancing age and demonstrated that females have significantly deeper sleep than males across several stages. OSD more accurately captured sleep depth reductions associated with SDB and increasing levels of cognitive impairment, showing significant reductions across all non-REM stages in patients with an increased level of cognitive impairment. OSD as a data-driven measure of sleep depth correlates strongly with arousal probability and effectively captures variations associated with age, sex, SDB and cognitive impairment. The results validate depth as an important dimension of sleep. OSD and ORP provide a nuanced understanding of sleep architecture with physiological and pathological implications.

EEG spectral analysis of nighttime sleep and daytime MSLTs and neurocognitive evaluations in subjects with co-morbid insomnia and OSA

BACKGROUND

Chronic insomnia and obstructive sleep apnea commonly co-occur. Few studies have explored the neurophysiological and neurocognitive characteristics of COMISA, which could help guide improving treatment diagnostic tools and determining novel therapeutic targets. This study aims to explore the neurophysiological and neurocognitive characteristics of COMISA using electroencephalographic (EEG) spectral analysis and subjective and objective neurocognitive measurements.

METHODS

Participants were from our community recruited OSA-insomnia-COMISA cohort with 206 included for our current analysis including 74 chronic insomniacs (CIs), 55 OSA patients and 77 COMISA patients. Standard polysomnography (PSG) and multiple sleep latency tests (MSLTs) were recorded and used to obtain relative EEG spectral power in each sleep stage during PSG and each session during MSLTs. A series of subjective and objective neurocognitive tests were conducted to evaluate executive function, attention, retrospective and prospective memory and meta-cognition.

RESULTS

In PSG and MSLTs, COMISA patients showed combined EEG power characteristics of both CIs and OSA. Specifically, COMISA patients exhibited similar EEG spectral characteristics to CIs, with decreased delta and increased alpha and beta power in NREM sleep stages, and increased beta power in REM and MSLTs. Similar to the EEG spectral power profile of OSA, COMISA patients showed increased delta power in REM and MSLTs. Compared to OSA patients, COMISA patients exhibited worse subjectively measured attention and meta-cognition related to negative beliefs about uncontrollability and danger of worry (NEG), which were positively associated with ISI scores.

CONCLUSIONS

The EEG spectral power characteristics of COMISA patients in overnight PSG and daytime MSLT appear to be the manifestation of elements of both CIs and OSA. However, the neurocognitive features of COMISA patients in subjectively measured attention and NEG meta-cognition were primarily affected by chronic insomnia.

Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. We propose a deep learning method to annotate continuous sleep depth index (SDI) with existing discrete sleep staging labels, using polysomnography from over 10,000 recordings across four large-scale cohorts. The results showcased a strong correlation between the decrease in sleep depth index and the increase in duration of arousal. Case studies indicated that SDI captured more nuanced sleep structures than conventional sleep staging. Clustering based on the digital biomarkers extracted from the SDI identified two subtypes of sleep, where participants in the disturbed subtype had a higher prevalence of several poor health conditions and were associated with a 33% increased risk of mortality and a 38% increased risk of fatal coronary heart disease. Our study underscores the utility of SDI in revealing more detailed sleep structures and yielding novel digital biomarkers for sleep medicine.

Comparative analysis of sleep physiology using qualitative and quantitative criteria for insomnia symptoms

Despite decades of research, defining insomnia remains challenging due to its complex and variable nature. Various diagnostic systems emphasize the chronic nature of insomnia and its impact on daily functioning, relying heavily on patient self-reporting due to limitations in objective measures such as polysomnography (PSG). Discrepancies between subjective experiences and objective PSG results highlight the need for more nuanced approaches, such as electroencephalogram (EEG) spectral analysis, which reveals distinct patterns of high-frequency activity in individuals with insomnia. This study explores EEG markers of insomnia by integrating subjective reports with objective physiological markers, specifically ORP (Odds-Ratio-Product) and spectral features, to address inconsistencies found in previous research and clinical settings. Qualitative and quantitative definitions of insomnia are contrasted to highlight differences in sleep architecture and EEG characteristics. The research aims to determine whether groups defined by weekly frequency and daily duration of symptoms have different distribution patterns and which physiological characteristics best distinguish insomnia patients from controls. Our findings suggest that ORP, as a dependent variable, captures the most significant differences in the independent variables across the model. Elevated beta power in insomnia patients indicates increased cortical arousal, supporting the perspective of insomnia as a hyperarousal disorder. Future research should focus on using ORP to enhance the understanding of sleep disturbances in insomnia. Comprehensive evaluation of insomnia requires integrating qualitative, quantitative, and neurophysiological data to fully understand its impact on sleep architecture and quality.

Evaluation of Sleep Quality in Clinical Practice

A major problem in clinical sleep medicine is that the most comprehensive and sophisticated investigative tool, the polysomnography, fails to provide a reason for the patient's complaints in all but those with sleep apnea and movement disorders. The reasons why conventional metrics of sleep quality are of limited value are discussed in detail. This is followed by description of several well-established features that have not been implemented in clinical practice because of the impracticality of doing the measurements visually. Automation is pending. Finally, several automated features recently derived from spectral analysis of the electroencephalogram were described.

Artificial Intelligence Can Drive Sleep Medicine

This article explores the transformative role of artificial intelligence (AI) in sleep medicine, highlighting its applications in detecting sleep microstructure patterns and integrating novel metrics. AI enhances diagnostic accuracy and objectivity, addressing inter-rater variability. AI also facilitates the classification of sleep disorders and the prediction of health outcomes. AI can drive sleep medicine to achieve deeper insights into sleep's impact on health, leading to personalized treatment strategies and improved patient care.

Association of Novel EEG Biomarkers of Sleep Depth and Cortical Arousability with Cardiac Autonomic Modulation in Adolescents

STUDY OBJECTIVES

To examine the developmental association of the odds ratio product (ORP), an electroencephalographic measure of sleep depth, during non-rapid eye movement (NREM) sleep with 24-hour heart rate variability (HRV), an electrocardiographic measure of cardiac autonomic modulation (CAM), in the transition to adolescence.

METHODS

Leveraging data from the Penn State Child Cohort, we performed longitudinal analyses on 313 children (median [Md] age 9 years) followed-up after Md=7.4y and cross-sectional analyses on 344 adolescents (Md=16y). We extracted ORP during NREM sleep and in the 9 seconds following cortical arousals (ORP-9) from 9-hour, in-lab polysomnography, and frequency- and time-domain HRV indices from 24-h Holter ECG monitoring. Longitudinal and cross-sectional, multivariable-adjusted, regression models examined the association between ORP and ORP-9 with adolescent 24-h HRV indices.

RESULTS

Longitudinally, a greater increase in ORP-9 since childhood was associated with lower daytime Log-LF, SDNN, RMSSD and higher HR in adolescence (p<0.05). A greater increase in ORP since childhood was associated with lower nighttime Log-LF and SDNN (p<0.05). Cross-sectionally, higher ORP and ORP-9 were associated with lower daytime and nighttime Log-LF, SDNN or RMSSD and higher HR within adolescence (p<0.05).

CONCLUSIONS

A greater increase in cortical arousability since childhood is a strong developmental predictor of daytime cardiac autonomic imbalance in adolescence. Shallower sleep depth additionally arises as a proximal determinant of both daytime and nighttime cardiac autonomic imbalance within adolescence. These data suggest a coupling between fine-grained spectral measures of the sleeping brain and those of CAM, which may inform sleep-related cardiovascular risk early in life.

Association between sleep microarchitecture and cognition in obstructive sleep apnea

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) increases the risk of cognitive impairment. Measures of sleep microarchitecture from EEG may help identify patients at risk of this complication.

METHODS

Participants with suspected OSA (n = 1142) underwent in-laboratory polysomnography and completed sleep and medical history questionnaires, and tests of global cognition (Montreal Cognitive Assessment, MoCA), memory (Rey Auditory Verbal Learning Test, RAVLT) and information processing speed (Digit-Symbol Coding, DSC). Associations between cognitive scores and stage 2 non-rapid eye movement (NREM) sleep spindle density, power, frequency and %-fast (12-16Hz), odds-ratio product (ORP), normalized EEG power (EEGNP), and the delta:alpha ratio were assessed using multivariable linear regression (MLR) adjusted for age, sex, education, and total sleep time. Mediation analyses were performed to determine if sleep microarchitecture indices mediate the negative effect of OSA on cognition.

RESULTS

All spindle characteristics were lower in participants with moderate and severe OSA (p ≤ .001, vs. no/mild OSA) and positively associated with MoCA, RAVLT, and DSC scores (false discovery rate corrected p-value, q ≤ 0.026), except spindle power which was not associated with RAVLT (q = 0.185). ORP during NREM sleep (ORPNREM) was highest in severe OSA participants (p ≤ .001) but neither ORPNREM (q ≥ 0.230) nor the delta:alpha ratio were associated with cognitive scores in MLR analyses (q ≥ 0.166). In mediation analyses, spindle density and EEGNP (p ≥ .048) mediated moderate-to-severe OSA's negative effect on MoCA scores while ORPNREM, spindle power, and %-fast spindles mediated OSA's negative effect on DSC scores (p ≤ .018).

CONCLUSIONS

Altered spindle activity, ORP and normalized EEG power may be important contributors to cognitive deficits in patients with OSA.

Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm

Purpose

To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.

Methods

In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. One hundred and twenty training sets and 80 test sets were randomly composed of the first 200 groups of data from the SHH1 database. The polysomnography (PSG) data of 20 real individuals in the clinic were selected as the experimental data. The C3 electroencephalogram (EEG), left and right electrooculogram (EOG), electromyogram (EMG), and other signals were analyzed. Finally, the stages were adjusted based on human sleep laws. The standard staging of the database and the doctor's diagnosis staging was used as the standard.

Results

The SHHS1 database test results were as follows: the average accuracy was 83.24%, the precision and recall of Stage Wake and Stage 2 NREM sleep (N2) were over 80%, and the precision, F1-Score and recall of Stage 3 NREM sleep (N3) and Rapid Eye Movement (REM) were more than 70%. The clinical data test results were as follows: the average accuracy rate was 76.37%; for Wake and N3, the precision reached 85%; for Wake, N2, and REM, the recall rate reached over 70%; for Wake, the F-1 Score reached over 90%.

Conclusion

This study shows that the sleep staging results of the algorithm for the database and clinical data were similar. The staging results meet the requirements at the medical level.

Physiologic Consequences of Upper Airway Obstruction in Sleep Apnea

OSA is diagnosed and managed by a metric called the apnea-hypopnea index (AHI). The AHI quantifies the number of respiratory events (apnea or hypopnea), disregarding important information on the characteristics and physiologic consequences of respiratory events, including degrees of ventilatory deficit and associated hypoxemia, cardiac autonomic response, and cortical activity. The oversimplification of the disorder by the AHI is considered one of the reasons for divergent findings on the associations of OSA and cardiovascular disease (CVD) in observational and randomized controlled trial studies. Prospective observational cohort studies have demonstrated strong associations of OSA with several cardiovascular diseases, and randomized controlled trials of CPAP intervention have not been able to detect a benefit of CPAP to reduce the risk of CVD. Over the last several years, novel methodologies have been proposed to better quantify the magnitude of OSA-related breathing disturbance and its physiologic consequences. As a result, stronger associations with cardiovascular and neurocognitive outcomes have been observed. In this review, we focus on the methods that capture polysomnographic heterogeneity of OSA.

Short-term and long-term phenotypic stability of actigraphic sleep metrics involving repeated sleep loss and recovery

For the first time, we determined whether actigraphic-assessed sleep measures show inter-individual differences and intra-individual stability during baseline (BL) and recovery (REC) phases surrounding repeated total sleep deprivation (TSD). We conducted a 5-day experiment at Months 2 and 4 in two separate studies (N = 11). During each experiment, sleep measures were collected via wrist actigraphy during two BL 8 h time-in-bed (TIB) nights (B1, B2) and during two REC 8-10 h TIB nights (R1, R2). Intraclass correlation coefficients (ICCs) assessed actigraphic measure long-term stability between 2 and 4 months for (1) the pre-experimental phase before BL; and (2) the BL (B1 + B2), REC (R1 + R2), and BL and REC average (BL + REC) phases; and short-term stability at Month 2 and at Month 4; and (3) between B1 versus B2 and R1 versus R2 in each 5-day experiment. Nearly all ICCs during the pre-experimental, BL, REC, and BL + REC phases were moderate to almost perfect (0.446-0.970) between Months 2 and 4. B1 versus B2 ICCs were more stable (0.440-0.899) than almost all R1 versus R2 ICCs (-0.696 to 0.588) at Month 2 and 4. Actigraphic sleep measures show phenotypic long-term stability during BL and REC surrounding repeated TSD between 2 and 4 months. Furthermore, within each 5-day experiment at Month 2 and 4, the two BL nights before TSD were more stable than the two REC nights following TSD, likely due to increased R1 homeostatic pressure. Given the consistency of actigraphic measures across the short-term and long-term, they can serve as biomarkers to predict physiological and neurobehavioral responses to sleep loss.

Development and validation of a novel sleep health score in the sleep heart health study

BACKGROUND

There is a lack of consensus in evaluating multidimensional sleep health, especially concerning its implication for mortality. A validated multidimensional sleep health score is the foundation of effective interventions.

METHODS

We obtained data from 5706 participants in the Sleep Heart Health Study. First, random forest-recursive feature elimination algorithm was used to select potential predictive variables. Second, a sleep composite score was developed based on the regression coefficients from a Cox proportional hazards model evaluating the associations between selected sleep-related variables and mortality. Last, we validated the score by constructing Cox proportional hazards models to assess its association with mortality.

RESULTS

The mean age of participants was 63.2 years old, and 47.6% (2715/5706) were male. Six sleep variables, including average oxygen saturation (%), spindle density (C3), sleep efficiency (%), spindle density (C4), percentage of fast spindles (%) and percentage of rapid eye movement (%) were selected to construct this multidimensional sleep health score. The average sleep composite score in participants was 6.8 of 22 (lower is better). Participants with a one-point increase in sleep composite score had an 10% higher risk of death (hazard ratio = 1.10, 95% confidence interval: 1.08-1.12).

CONCLUSIONS

This study constructed and validated a novel multidimensional sleep health score to better predict death based on sleep, with significant associations between sleep composite score and all-cause mortality. Integrating questionnaire information and sleep microstructures, our sleep composite score is more appropriately applied for mortality risk stratification.

Adaptive and Compensatory Neural Signatures in Fibromyalgia: An Analysis of Resting-State and Stimulus-Evoked EEG Oscillations

This study aimed to investigate clinical and physiological predictors of brain oscillatory activity in patients with fibromyalgia (FM), assessing resting-state power, event-related desynchronization (ERD), and event-related synchronization (ERS) during tasks. We performed a cross-sectional analysis, including clinical and neurophysiological data from 78 subjects with FM. Multivariate regression models were built to explore predictors of electroencephalography bands. Our findings show a negative correlation between beta oscillations and pain intensity; fibromyalgia duration is positively associated with increased oscillatory power at low frequencies and in the beta band; ERS oscillations in the theta and alpha bands seem to be correlated with better symptoms of FM; fatigue has a signature in the alpha band-a positive relationship in resting-state and a negative relationship in ERS oscillations. Specific neural signatures lead to potential clusters of neural adaptation, in which beta oscillatory activity in the resting state represents a more adaptive activity when pain levels are low and stimulus-evoked oscillations at lower frequencies are likely brain compensatory mechanisms. These neurophysiological changes may help to understand the impact of long-term chronic pain in the central nervous system and the descending inhibitory system in fibromyalgia subjects.

A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

Purpose

Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency.

Methods

The study involved 50 normal and 100 obstructive sleep apnea-hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM).

Results

The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%.

Conclusion

A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.

Polysomnographic characteristics of excessive daytime sleepiness phenotypes in obstructive sleep apnea: results from the international sleep apnea global interdisciplinary consortium

STUDY OBJECTIVES

Excessive daytime sleepiness (EDS) is a major symptom of obstructive sleep apnea (OSA). Traditional polysomnographic (PSG) measures only partially explain EDS in OSA. This study analyzed traditional and novel PSG characteristics of two different measures of EDS among patients with OSA.

METHODS

Sleepiness was assessed using the Epworth Sleepiness Scale (>10 points defined as "risk of dozing") and a measure of general sleepiness (feeling sleepy ≥ 3 times/week defined as "feeling sleepy"). Four sleepiness phenotypes were identified: "non-sleepy," "risk of dozing only," "feeling sleepy only," and "both at risk of dozing and feeling sleepy."

RESULTS

Altogether, 2083 patients with OSA (69% male) with an apnea-hypopnea index (AHI) ≥ 5 events/hour were studied; 46% were "non-sleepy," 26% at "risk of dozing only," 7% were "feeling sleepy only," and 21% reported both. The two phenotypes at "risk of dozing" had higher AHI, more severe hypoxemia (as measured by oxygen desaturation index, minimum and average oxygen saturation [SpO2], time spent < 90% SpO2, and hypoxic impacts) and they spent less time awake, had shorter sleep latency, and higher heart rate response to arousals than "non-sleepy" and "feeling sleepy only" phenotypes. While statistically significant, effect sizes were small. Sleep stages, frequency of arousals, wake after sleep onset and limb movement did not differ between sleepiness phenotypes after adjusting for confounders.

CONCLUSIONS

In a large international group of patients with OSA, PSG characteristics were weakly associated with EDS. The physiological measures differed among individuals characterized as "risk of dozing" or "non-sleepy," while "feeling sleepy only" did not differ from "non-sleepy" individuals.

Sleep electroencephalography biomarkers of cognition in obstructive sleep apnea

Obstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea-hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow-wave activity, sleep spindles, K-complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high-density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.

Contribution of obstructive sleep apnea to disrupted sleep in a large clinical cohort of patients with suspected obstructive sleep apnea

STUDY OBJECTIVES

The response of sleep depth to CPAP in patients with OSA is unpredictable. The odds-ratio-product (ORP) is a continuous index of sleep depth and wake propensity that distinguishes different sleep depths within sleep stages, and different levels of vigilance during stage wake. When expressed as fractions of time spent in different ORP deciles, nine distinctive patterns are found. Only three of these are associated with OSA. We sought to determine whether sleep depth improves on CPAP exclusively in patients with these three ORP patterns.

METHODS

ORP was measured during the diagnostic and therapeutic components of 576 split-night polysomnographic (PSG) studies. ORP architecture in the diagnostic section was classified into one of the nine possible ORP patterns and the changes in sleep architecture were determined on CPAP for each of these patterns. ORP architecture was similarly determined in the first half of 760 full-night diagnostic PSG studies and the changes in the second half were measured to control for differences in sleep architecture between the early and late portions of sleep time in the absence of CPAP.

RESULTS

Frequency of the three ORP patterns increased progressively with the apnea-hypopnea index. Sleep depth improved significantly on CPAP only in the three ORP patterns associated with OSA. Changes in CPAP in the other six patterns, or in full diagnostic PSG studies, were insignificant or paradoxical.

CONCLUSIONS

ORP architecture types can identify patients in whom OSA adversely affects sleep and whose sleep is expected to improve on CPAP therapy.

Enhancing OSA Assessment with Explainable AI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-6. [PMID: 38083271 DOI: 10.1109/embc40787.2023.10341035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Explainable Artificial Intelligence (xAI) is a rapidly growing field that focuses on making deep learning models interpretable and understandable to human decision-makers. In this study, we introduce xAAEnet, a novel xAI model applied to the assessment of Obstructive Sleep Apnea (OSA) severity. OSA is a prevalent sleep disorder that can lead to numerous medical conditions and is currently assessed using the Apnea-Hypopnea Index (AHI). However, AHI has been criticized for its inability to accurately estimate the effect of OSAs on related medical conditions. To address this issue, we propose a human-centric xAI approach that emphasizes similarity between apneic events as a whole and reduces subjectivity in diagnosis by examining how the model makes its decisions. Our model was trained and tested on a dataset of 60 patients' Polysomnographic (PSG) recordings. Our results demonstrate that the proposed model, xAAEnet, outperforms models with traditional architectures such as convolutional regressor, autoencoder (AE), and variational autoencoder (VAE). This study highlights the potential of xAI in providing an objective OSA severity scoring method.Clinical relevance- This study provides an objective OSA severity scoring technique which could improve the management of apneic patients in clinical practice.

The Contribution of Sleep Texture in the Characterization of Sleep Apnea

Obstructive sleep apnea (OSA) is multi-faceted world-wide-distributed disorder exerting deep effects on the sleeping brain. In the latest years, strong efforts have been dedicated to finding novel measures assessing the real impact and severity of the pathology, traditionally trivialized by the simplistic apnea/hypopnea index. Due to the unavoidable connection between OSA and sleep, we reviewed the key aspects linking the breathing disorder with sleep pathophysiology, focusing on the role of cyclic alternating pattern (CAP). Sleep structure, reflecting the degree of apnea-induced sleep instability, may provide topical information to stratify OSA severity and foresee some of its dangerous consequences such as excessive daytime sleepiness and cognitive deterioration. Machine learning approaches may reinforce our understanding of this complex multi-level pathology, supporting patients' phenotypization and easing in a more tailored approach for sleep apnea.

A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.

Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes

Study Objectives

Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not apparent from traditional metrics.

Methods

We analyzed records from the Sleep-Heart-Health-Study and a study of performance deficit following sleep deprivation. ORP of all 30-second epochs in each PSG and percent of epochs in each decile of ORPs range were calculated. Percentage of epochs in deep sleep (ORP < 0.50) and in full-wakefulness (ORP > 2.25) were each assigned a rank, 1–3, representing first and second digits, respectively, of nine distinct types (“1,1”, “1,2” … ”3,3”). Prevalence of each type in clinical groups and their associations with demographics, sleepiness (Epworth-Sleepiness-Scale, ESS) and quality of life (QOL; Short-Form-Health-Survey-36) were determined.

Results

Three types (“1,1”, “1,2”, “1,3”) were prevalent in OSA and were associated with reduced QOL. Two (“1,3” and “2,3”) were prevalent in insomnia with short-sleep-duration (insomnia-SSD), but only “1,3” was associated with poor sleep depth and reduced QOL, suggesting two phenotypes in insomnia-SSD. ESS was high in types “1,1” and “1,2”, and low in “1,3” and “2,3”. Prevalence of some types increased with age while in others it decreased. Other types were either rare (“1,1” and “3,3”) or high (“2,2”) at all ages.

Conclusions

The proposed ORP histogram offers specific and unique information on the underlying neurophysiological characteristics of sleep disorders not captured by routine metrics, with potential of advancing diagnosis and management of these disorders.

A novel EEG marker predicts perceived sleepiness and poor sleep quality

STUDY OBJECTIVES

To determine if a novel EEG-derived continuous index of sleep depth/alertness, the odds ratio product (ORP), predicts self-reported daytime sleepiness and poor sleep quality in two large population-based cohorts.

METHODS

ORP values which range from 0 (deep sleep) to 2.5 (fully alert) were calculated in 3s intervals during awake periods (ORPwake) and NREM sleep (ORPNREM) determined from home sleep studies in the HypnoLaus (N = 2162: 1106 females, 1056 males) and men androgen inflammation lifestyle environment and stress (MAILES) cohorts (N = 754 males). Logistic regression was used to examine associations between ORPwake, ORPNREM, and traditional polysomnography measures (as comparators) with excessive sleepiness (Epworth sleepiness scale >10) and poor sleep quality (Pittsburgh sleep quality index >5) and insomnia symptoms.

RESULTS

High ORPwake was associated with a ~30% increase in poor sleep quality in both HypnoLaus (odds ratio, OR, and 95% CI) 1.28 (1.09, 1.51), and MAILES 1.36 (1.10, 1.68). High ORPwake was also associated with a ~28% decrease in excessive daytime sleepiness in the MAILES dataset. ORPNREM was associated with a ~30% increase in poor sleep quality in HypnoLaus but not in MAILES. No consistent associations across cohorts were detected using traditional polysomnography markers.

CONCLUSIONS

ORP, a novel EEG-derived metric, measured during wake periods predicts poor sleep quality in two independent cohorts. Consistent with insomnia symptomatology of poor perceived sleep in the absence of excessive daytime sleepiness, ORPwake may provide valuable objective mechanistic insight into physiological hyperarousal.

Adherence Index: sleep depth and nocturnal hypoventilation predict long-term adherence with positive airway pressure therapy in severe obstructive sleep apnea

STUDY OBJECTIVES

Treatment of obstructive sleep apnea with positive airway pressure (PAP) devices is limited by poor long-term adherence. Early identification of individual patients' probability of long-term PAP adherence would help in their management. We determined whether conventional polysomnogram (PSG) scoring and measures of sleep depth based on the odds ratio product would predict adherence with PAP therapy 12 months after it was started.

METHODS

Patients with obstructive sleep apnea referred to an academic sleep center had split-night PSG, arterial blood gases, and a sleep questionnaire. Multiple linear regression analysis of conventional PSG scoring and the odds ratio product both during diagnostic PSG and PAP titration provided an "Adherence Index," which was correlated with PAP use 12 months later.

RESULTS

Patients with obstructive sleep apnea (n = 236, apnea-hypopnea index 72.2 ± 34.1 events/h) were prescribed PAP therapy (82% received continuous PAP, 18% received bilevel PAP). Each patient's adherence with PAP therapy 12 months later was categorized as "never used," "quit using," "poor adherence," and "good adherence." PSG measures that were most strongly correlated with PAP adherence were apnea-hypopnea index and odds ratio product during nonrapid eye movement sleep; the additional contribution of nocturnal hypoxemia to this correlation was confined to those with chronic hypoventilation treated with bilevel PAP. The Adherence Index derived from these measures, during both diagnostic PSG and PAP titration, was strongly correlated with PAP adherence 12 months later.

CONCLUSIONS

Long-term adherence with PAP therapy can be predicted from diagnostic PSG in patients with severe obstructive sleep apnea, which may facilitate a precision-based approach to PAP management.

CITATION

Younes MK, Beaudin AE, Raneri JK, Gerardy BJ, Hanly PJ. Adherence Index: sleep depth and nocturnal hypoventilation predict long-term adherence with positive airway pressure therapy in severe obstructive sleep apnea. J Clin Sleep Med. 2022:18(8):1933-1944.

Association of a novel EEG metric of sleep depth/intensity with attention-deficit/hyperactivity, learning, and internalizing disorders and their pharmacotherapy in adolescence

STUDY OBJECTIVES

Psychiatric/learning disorders are associated with sleep disturbances, including those arising from abnormal cortical activity. The odds ratio product (ORP) is a standardized electroencephalogram metric of sleep depth/intensity validated in adults, while ORP data in youth are lacking. We tested ORP as a measure of sleep depth/intensity in adolescents with and without psychiatric/learning disorders.

METHODS

Four hundred eighteen adolescents (median 16 years) underwent a 9-hour, in-lab polysomnography. Of them, 263 were typically developing (TD), 89 were unmedicated, and 66 were medicated for disorders including attention-deficit/hyperactivity (ADHD), learning (LD), and internalizing (ID). Central ORP during non-rapid eye movement (NREM) sleep was the primary outcome. Secondary/exploratory outcomes included central and frontal ORP during NREM stages, in the 9-seconds following arousals (ORP-9), in the first and second halves of the night, during REM sleep and wakefulness.

RESULTS

Unmedicated youth with ADHD/LD had greater central ORP than TD during stage 3 and in central and frontal regions during stage 2 and the second half of the sleep period, while ORP in youth with ADHD/LD on stimulants did not significantly differ from TD. Unmedicated youth with ID did not significantly differ from TD in ORP, while youth with ID on antidepressants had greater central and frontal ORP than TD during NREM and REM sleep, and higher ORP-9.

CONCLUSIONS

The greater ORP in unmedicated youth with ADHD/LD, and normalized levels in those on stimulants, suggests ORP is a useful metric of decreased NREM sleep depth/intensity in ADHD/LD. Antidepressants are associated with greater ORP/ORP-9, suggesting these medications induce cortical arousability.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

A Novel EEG Derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk

RATIONALE

Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance.

OBJECTIVES

This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics.

METHODS

Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an ~11y follow-up period.

RESULTS

Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratios 1.32 [95% confidence interval 1.14, 1.50], after adjusting for total sleep time and other clinical and life-style related covariates including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5h to 4.25h. Conventional measures of sleep quality, including wake after sleep onset and arousal index were not predictive of all-cause mortality.

CONCLUSIONS

Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health and safety outcomes has considerable potential to provide unique neurophysiological insight.