Nocturnal oxygen resaturation parameters are associated with cardiorespiratory comorbidities.

BACKGROUND: There are strong associations between oxygen desaturations and cardiovascular outcomes. Additionally, oxygen resaturation rates are linked to excessive daytime sleepiness independent of oxygen desaturation severity. No studies have yet looked at the independent effects of comorbidities or medications on resaturation parameters. METHODS: The Sleep Heart Health Study data was utilised to derive oxygen saturation parameters from 5804 participants. Participants with a history of comorbidities or medication usage were compared against healthy participants with no comorbidity/medication history. RESULTS: 4293 participants (50.4% female, median age 64 years) were included in the analysis. Females recorded significantly faster resaturation rates (mean 0.61%/s) than males (mean 0.57%/s, p < 0.001), regardless of comorbidities. After adjusting for demographics, sleep parameters, and desaturation parameters, resaturation rate was reduced with hypertension (-0.09 (95% CI -0.16, -0.03)), myocardial infarction (-0.13 (95% CI -0.21, -0.04)) and heart failure (-0.19 (95% CI -0.33, -0.05)), or when using anti-hypertensives (-0.10 (95% CI -0.17, -0.03)), mental health medications (-0.18 (95% CI -0.27, -0.08)) or anticoagulants (-0.41 (95% CI -0.56, -0.26)). Desaturation to Resaturation ratio for duration was decreased with mental health (-0.21 (95% CI -0.34, -0.08)) or diabetic medications (-0.24 (95% CI -0.41, -0.07)), and desaturation to resaturation ratio for area decreased with heart failure (-0.25 (95% CI -0.42, -0.08)). CONCLUSIONS: Comorbidities and medications significantly affect nocturnal resaturation parameters, independent of desaturation parameters. However, the causal relationship remains unclear. Further research can enhance our knowledge and develop more precise and safer interventions for individuals affected by certain comorbidities.

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages.

OBJECTIVE: The current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages. METHODS: Type II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages. RESULTS: The results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 µv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages. CONCLUSIONS: Technical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation.

OBJECTIVE: The aim of this study was to assess how the photoplethysmogram frequency and amplitude responses to arousals from sleep differ between arousals caused by apneas and hypopneas with and without blood oxygen desaturations, and spontaneous arousals. Stronger arousal causes were hypothesized to lead to larger and faster responses. METHODS AND PROCEDURES: Photoplethysmogram signal segments during and around respiratory and spontaneous arousals of 876 suspected obstructive sleep apnea patients were analyzed. Logistic functions were fit to the mean instantaneous frequency and instantaneous amplitude of the signal to detect the responses. Response intensities and timings were compared between arousals of different causes. RESULTS: The majority of the studied arousals induced photoplethysmogram responses. The frequency response was more intense ([Formula: see text]) after respiratory than spontaneous arousals, and after arousals caused by apneas compared to those caused by hypopneas. The amplitude response was stronger ([Formula: see text]) following hypopneas associated with blood oxygen desaturations compared to those that were not. The delays of these responses relative to the electroencephalogram arousal start times were the longest ([Formula: see text]) after arousals caused by apneas and the shortest after spontaneous arousals and arousals caused by hypopneas without blood oxygen desaturations. CONCLUSION: The presence and type of an airway obstruction and the presence of a blood oxygen desaturation affect the intensity and the timing of photoplethysmogram responses to arousals from sleep. CLINICAL IMPACT: The photoplethysmogram responses could be used for detecting arousals and assessing their intensity, and the individual variation in the response intensity and timing may hold diagnostically significant information.

The role of physical activity on obstructive sleep apnea severity and hypoxic load, and the mismatch between subjective and objective physical activity assessments.

Obesity is the primary risk factor for the development of obstructive sleep apnea, and physical inactivity plays an important role. However, most studies have either only evaluated physical activity subjectively or objectively in obstructive sleep apnea. The objectives of this study were: (i) to assess the relationship between obstructive sleep apnea severity (both apnea-hypopnea index and desaturation parameters) and both objectively and subjectively measured physical activity after adjustment for anthropometry and body composition parameters; and (ii) to assess the relationship between objective and subjective physical activity parameters and whether obstructive sleep apnea severity has a modulatory effect on this relationship. Fifty-four subjects (age 47.7 ± 15.0 years, 46% males) were categorized into groups according to obstructive sleep apnea severity: no obstructive sleep apnea; mild obstructive sleep apnea; and moderate-to-severe obstructive sleep apnea. All subjects were evaluated with subjective and objective physical activity, anthropometric and body composition measurements, and 3-night self-applied polysomnography. A one-way ANOVA was used to evaluate the differences between the three obstructive sleep apnea severity groups and multiple linear regression to predict obstructive sleep apnea severity. Differences in subjectively reported sitting time (p ≤ 0.004) were found between participants with moderate-to-severe obstructive sleep apnea, and those with either mild or no obstructive sleep apnea (p = 0.004). Age, body mass index and neck circumference explained 63.3% of the variance in the apnea-hypopnea index, and age, body mass index and visceral adiposity explained 67.8% of the variance in desaturation parameters. The results showed that the person's physical activity does not affect obstructive sleep apnea severity. A weak correlation was found between objective and subjective physical activity measures, which could be relevant for healthcare staff encouraging patients with obstructive sleep apnea to increase their physical activity.

Beat-to-beat cardiac repolarization lability increases during hypoxemia and arousals in obstructive sleep apnea patients.

Obstructive sleep apnea (OSA) is associated with the progression of cardiovascular diseases, arrhythmias, and sudden cardiac death (SCD). However, the acute impacts of OSA and its consequences on heart function are not yet fully elucidated. We hypothesized that desaturation events acutely destabilize ventricular repolarization, and the presence of accompanying arousals magnifies this destabilization. Ventricular repolarization lability measures, comprising heart rate corrected QT (QTc), short-time-variability of QT (STVQT), and QT variability index (QTVI), were calculated before, during, and after 20,955 desaturations from lead II electrocardiography signals of 492 patients with suspected OSA (52% men). Variations in repolarization parameters were assessed during and after desaturations, both with and without accompanying arousals, and groupwise comparisons were performed based on desaturation duration and depth. Regression analyses were used to investigate the influence of confounding factors, comorbidities, and medications. The standard deviation (SD) of QT, mean QTc, SDQTc, and STVQT increased significantly (P < 0.01), whereas QTVI decreased (P < 0.01) during and after desaturations. The changes in SDQT, mean QTc, SDQTc, and QTVI were significantly amplified (P < 0.01) in the presence of accompanying arousals. Desaturation depth was an independent predictor of increased SDQTc (ß = 0.405, P < 0.01), STVQT (ß = 0.151, P < 0.01), and QTVI (ß = 0.009, P < 0.01) during desaturation. Desaturations cause acute changes in ventricular repolarization, with deeper desaturations and accompanying arousals independently contributing to increased ventricular repolarization lability. This may partially explain the increased risk of arrhythmias and SCD in patients with OSA, especially when the OSA phenotype includes high hypoxic load and fragmented sleep.NEW & NOTEWORTHY Nocturnal desaturations are associated with increased ventricular repolarization lability. Deeper desaturations with accompanying arousals increase the magnitude of alterations, independent of confounding factors, comorbidities, and medications. Changes associated with desaturations can partially explain the increased risk of arrhythmias and sudden cardiac death in patients with OSA, especially in patients with high hypoxic load and fragmented sleep. This highlights the importance of detailed electrocardiogram analytics for patients with OSA.

Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index.

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHITAT) and TST-based REI (REITST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHITAT, and REITST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHITAT, the accuracies were 68.4% and 85.9%, and based on REITST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REITST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REITST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity.

Multicentre sleep-stage scoring agreement in the Sleep Revolution project.

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.

Acute Cardiorespiratory Coupling Impairment in Worsening Sleep Apnea-Related Intermittent Hypoxemia.

OBJECTIVE: Hypoxic load is one of the main characteristics of obstructive sleep apnea (OSA) contributing to sympathetic overdrive and weakened cardiorespiratory coupling (CRC). Whether this association changes with increasing hypoxic load has remained obscure. Therefore, we aimed to study our hypothesis that increasing hypoxic load acutely decreases the CRC. METHODS: We retrospectively analyzed the electrocardiography and nasal pressure signals in 5-min segment pairs (n = 36 926) recorded during clinical polysomnographies of 603 patients with suspected OSA. The segment pairs were pooled into five groups based on the hypoxic load severity described with the the total integrated area under the blood oxygen saturation curve during desaturations. In these severity groups, we determined the frequency-domain heart rate variability (HRV) parameters, the HRV and respiratory high-frequency (HF, 0.15-0.4 Hz) peaks, and the difference between those peaks. We also computed the spectral HF coherence between HRV and respiration in the HF band. RESULTS: The ratio of low-frequency (LF, 0.04-0.15 Hz) to HF power increased from 1.047 to 1.805 (p < 0.001); the difference between the HRV and respiratory HF peaks increased from 0.001 Hz to 0.039 Hz (p < 0.001); and the spectral coherence between HRV and respiration in the HF band decreased from 0.813 to 0.689 (p < 0.001) as the hypoxic load increased. CONCLUSION AND SIGNIFICANCE: The vagal modulation decreases and CRC weakens significantly with increasing hypoxic load. Thus, the hypoxic load could be utilized more thoroughly in contemporary OSA diagnostics to better assess the severity of OSA-related cardiac stress.

Review and perspective on sleep-disordered breathing research and translation to clinics.

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

Increased Flow Limitation During Sleep Is Associated With Increased Psychomotor Vigilance Task Lapses in Individuals With Suspected OSA.

BACKGROUND: Impaired daytime vigilance is an important consequence of OSA, but several studies have reported no association between objective measurements of vigilance and apnea-hypopnea index (AHI). Notably, the AHI does not quantify the degree of flow limitation, that is, the extent to which ventilation fails to meet intended ventilatory drive. RESEARCH QUESTION: Is flow limitation during sleep associated with daytime vigilance in OSA? STUDY DESIGN AND METHODS: Nine hundred ninety-eight participants with suspected OSA completed a 10-min psychomotor vigilance task (PVT) before same-night in-laboratory polysomnography. Flow limitation frequency (percent of flow-limited breaths) during sleep was quantified using airflow shapes (eg, fluttering and scooping) from nasal pressure airflow. Multivariable regression assessed the association between flow limitation frequency and the number of lapses (response times > 500 ms, primary outcome), adjusting for age, sex, BMI, total sleep time, depression, and smoking status. RESULTS: Increased flow limitation frequency was associated with decreased vigilance: a 1-SD (35.3%) increase was associated with 2.1 additional PVT lapses (95% CI, 0.7-3.7; P = .003). This magnitude was similar to that for age, where a 1-SD increase (13.5 years) was associated with 1.9 additional lapses. Results were similar after adjusting for AHI, hypoxemia severity, and arousal severity. The AHI was not associated with PVT lapses (P = .20). In secondary exploratory analysis, flow limitation frequency was associated with mean response speed (P = .012), median response time (P = .029), fastest 10% response time (P = .041), slowest 10% response time (P = .018), and slowest 10% response speed (P = .005). INTERPRETATION: Increased flow limitation during sleep was associated with decreased daytime vigilance in individuals with suspected OSA, independent of AHI. Flow limitation may complement standard clinical metrics in identifying individuals whose vigilance impairment most likely is explained by OSA.

Nasal Pressure Derived Airflow Limitation and Ventilation Measurements are Resilient to Reduced Signal Quality.

Obstructive sleep apnea is a disorder characterized by partial or complete airway obstructions during sleep. Our previously published algorithms use the minimally invasive nasal pressure signal routinely collected during diagnostic polysomnography (PSG) to segment breaths and estimate airflow limitation (using flow:drive) and minute ventilation for each breath. The first aim of this study was to investigate the effect of airflow signal quality on these algorithms, which can be influenced by oronasal breathing and signal-to-noise ratio (SNR). It was hypothesized that these algorithms would make inaccurate estimates when the expiratory portion of breaths is attenuated to simulate oronasal breathing, and pink noise is added to the airflow signal to reduce SNR. At maximum SNR and 0% expiratory amplitude, the average error was 2.7% for flow:drive, -0.5% eupnea for ventilation, and 19.7 milliseconds for breath duration (n = 257,131 breaths). At 20 dB and 0% expiratory amplitude, the average error was -15.1% for flow:drive, 0.1% eupnea for ventilation, and 28.4 milliseconds for breath duration (n = 247,160 breaths). Unexpectedly, simulated oronasal breathing had a negligible effect on flow:drive, ventilation, and breath segmentation algorithms across all SNRs. Airflow SNR ≥ 20 dB had a negligible effect on ventilation and breath segmentation, whereas airflow SNR ≥ 30 dB had a negligible effect on flow:drive. The second aim of this study was to explore the possibility of correcting these algorithms to compensate for airflow signal asymmetry and low SNR. An offset based on estimated SNR applied to individual breath flow:drive estimates reduced the average error to ≤ 1.3% across all SNRs at patient and breath levels, thereby facilitating for flow:drive to be more accurately estimated from PSGs with low airflow SNR.Clinical Relevance- This study demonstrates that our airflow limitation, ventilation, and breath segmentation algorithms are robust to reduced airflow signal quality.

Multi-centre arousal scoring agreement in the Sleep Revolution.

We investigated arousal scoring agreement within full-night polysomnography in a multi-centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second-by-second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04-0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second-by-second agreement was fair (Fleiss' kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07-0.68). Fleiss' kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes.

The economic cost of obstructive sleep apnea: A systematic review.

Obstructive sleep apnea (OSA) is a common disease associated with a high prevalence of costly comorbidities and accidents that add to the disease's economic impact. Although more attention has been focused on OSA in recent years, no previous systematic reviews have synthesized findings from existing studies that provide estimates of the economic cost of OSA. This study aims to summarize the findings of existing studies that provide estimates of the cost of OSA. Two bibliographic databases, PubMed and Scopus, were used to identify articles on the costs of OSA. The systematic literature review identified 5,938 publications, of which 31 met the inclusion criteria. According to the results, adjusted for inflation and converted to euros, the annual cost per patient ranged from €236 (the incremental cost of OSA) for New Zealand to €28,267 for the United States. The total annual cost per patient in Europe ranged from €1,669 to €5,186. OSA causes a significant burden on society, and OSA-related costs increase many years before the diagnosis and remain elevated for a long time after the diagnosis. Despite some well-conducted studies, the cost estimates for OSA are uncertain and specific to the context in which the study was conducted.

Reaction time in psychomotor vigilance task is related to hypoxic load in males with sleep apnea.

Oxygen saturation (SpO2 )-based parameters are more strongly linked to impaired daytime vigilance than the conventional diagnostic metrics in patients with obstructive sleep apnea (OSA). However, whether the association between SpO2 -based parameters and impaired daytime vigilance is modulated by sex, remains unknown. Hence, we investigated the interplay between sex and detailed SpO2 -based metrics and their association with impaired vigilance in patients with OSA. The study population consisted of 855 (473 males, 382 females) patients with suspected OSA who underwent overnight polysomnography and psychomotor vigilance task (PVT). The population was grouped by sex and divided into quartiles (Q1-Q4) based on median reaction times (RTs) in the PVT. In addition to conventional diagnostic metrics, desaturation severity (DesSev), fall severity (FallSev), and recovery severity (RecovSev) were compared between the sexes and between the best (Q1) and worst (Q4) performing quartiles by using cumulative distribution functions (CDFs). Additionally, sex-specific covariate-adjusted linear regression models were used to investigate the connection between the parameters and RTs. The CDFs showed significantly higher hypoxic load in Q4 in males compared to females. In addition, the DesSev (ß = 8.05, p < 0.01), FallSev (ß = 6.48, p = 0.02), RecovSev (ß = 9.13, p < 0.01), and Oxygen Desaturation Index (ß = 12.29, p < 0.01) were associated with increased RTs only in males. Conversely, the Arousal Index (ß = 10.75-11.04, p < 0.01) was associated with impaired vigilance in females. The severity of intermittent hypoxaemia was strongly associated with longer RTs in males whereas the Arousal Index had the strongest association in females. Thus, the impact of hypoxic load on impaired vigilance seems to be stronger in males than females.

Self-applied somnography: technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults.

Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin-electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%-43.9%, p < 0.001) and less absolute power (at 1-40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3-1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

Oxygen resaturation rate is significantly associated with objectively assessed excessive daytime sleepiness in suspected obstructive sleep apnoea patients.

INTRODUCTION: Commonly utilised metrics such as the apnoea-hypopnoea index show limited correlation to excessive daytime sleepiness (EDS). Oxygen desaturation parameters show better predictive power, however oxygen resaturation parameters have not yet been investigated. Oxygen resaturation may represent increased cardiovascular fitness and thus we hypothesized that a higher resaturation rate would be protective against EDS. METHODS: Oxygen saturation parameters were computed via ABOSA software for adult patients referred for polysomnography and multiple sleep latency test in Israel Loewenstein hospital 2001-2011. EDS was defined as a mean sleep latency (MSL) below 8 min. RESULTS: 1629 patients (75% male, 53% obese, median age of 54 years) were included for analysis. The average desaturation event nadir was 90.4% and resaturation rate 0.59%/second. Median MSL was 9.6 min, and 606 patients met criteria for EDS. Patients who were younger, female, and with larger desaturations had significantly higher resaturation rates (p < 0.001). In multivariate models, adjusted for age, sex, body mass index, and average desaturation depth, resaturation rate showed a significant negative correlation with MSL (z-score standardised beta, -1 (95%CI -0.49, -1.52)), and significantly increased odds ratio (OR) of EDS (OR, 1.28 (95%CI 1.07, 1.53)). The beta associated with resaturation rate was larger, though non-significantly, than that of desaturation depth (difference 0.36 (95% CI -1.34, 0.62), p = 0.470). CONCLUSION: Oxygen resaturation parameters show significant associations with objectively assessed EDS independent of desaturation parameters. Thus, resaturation and desaturation parameters may reflect differing underlying mechanistic pathways and both be considered novel and appropriate markers for assessing sleep-disordered breathing and associated outcomes.

Utilizing Envelope Analysis of a Nasal Pressure Signal for Sleep Apnea Severity Estimation.

Obstructive sleep apnea (OSA) severity assessment is based on manually scored respiratory events and their arbitrary definitions. Thus, we present an alternative method to objectively evaluate OSA severity independently of the manual scorings and scoring rules. A retrospective envelope analysis was conducted on 847 suspected OSA patients. Four parameters were calculated from the difference between the nasal pressure signal's upper and lower envelopes: average (AV), median (MD), standard deviation (SD), and coefficient of variation (CoV). We computed the parameters from the entirety of the recorded signals to perform binary classifications of patients using three different apnea-hypopnea index (AHI) thresholds (5-15-30). Additionally, the calculations were undertaken in 30-second epochs to estimate the ability of the parameters to detect manually scored respiratory events. Classification performances were assessed with areas under the curves (AUCs). As a result, the SD (AUCs ≥ 0.86) and CoV (AUCs ≥ 0.82) were the best classifiers for all AHI thresholds. Furthermore, non-OSA and severe OSA patients were separated well with SD (AUC = 0.97) and CoV (AUC = 0.95). Respiratory events within the epochs were identified moderately with MD (AUC = 0.76) and CoV (AUC = 0.82). In conclusion, envelope analysis is a promising alternative method by which to assess OSA severity without relying on manual scoring or the scoring rules of respiratory events.