Sleep-disordered Breathing Events Research Articles

The surge in heart rate and blood pressure at respiratory event termination is dampened in children with down syndrome

BackgroundChildren with Down syndrome (DS) have a high prevalence of sleep disordered breathing (SDB) and altered cardiovascular autonomic control. We aimed to analyze the effect of DS on the surge in heart rate (HR) and pulse transit time (PTT, an inverse surrogate measure of blood pressure change) at respiratory event termination. Methods44 children (3–19 y) with DS and 44 typically developing (TD) children matched for SDB severity, age and sex underwent overnight polysomnography. Multilevel modelling determined the effect of DS on HR and PTT changes between a 10s pre-event to the latter half of each respiratory event (late-event) and 15s post-event during NREM and REM, accounting for SDB severity and event length. ResultsThe children with DS had a significantly smaller % change in HR late-event to post-event (NREM: DS 26.4 % ± 17.5 % (mean ± SD), TD 30.7 % ± 21.0 %; REM DS 16.9 % ± 15.3 %, TD 21.0 % ± 14.0 %; p < 0.05 for both) compared with TD children for obstructive events, and central events (13.2 % ± 17.0 %, TD 18.8 % ± 17.0 %; p < 0.01) during REM. %change in PTT was significantly smaller in the DS group during NREM and REM from pre-event and late-event to post-event compared with TD children for obstructive and central events. ConclusionThese results suggest children with DS have dampened HR and BP responses to respiratory events compared with TD children. Whether this is symptomatic of autonomic dysfunction or a protective factor for the cardiovascular system in children with DS remains to be elucidated.

Impact of sleep-disordered breathing on overnight changes in arterial stiffness in patients with acute heart failure.

Overnight increases in arterial stiffness associated with sleep-disordered breathing may adversely affect patients with acute heart failure. Thus, we investigated overnight changes in arterial stiffness and their association with sleep-disordered breathing in patients hospitalized for acute heart failure. Consecutive patients with acute heart failure were enrolled. All participants underwent overnight full polysomnography following the initial improvement of acute signs and symptoms of acute heart failure. The arterial stiffness parameter, cardio-ankle vascular index (CAVI), was assessed before and after polysomnography. Overall, 60 patients (86.7% men) were analyzed. CAVI significantly increased overnight (from 8.4 ± 1.6 at night to 9.1 ± 1.7 in the morning, P < 0.001) in addition to systolic and diastolic blood pressure (from 114.1 mmHg to 121.6 mmHg, P < 0.001; and from 70.1 mmHg to 78.2 mmHg, P < 0.001, respectively). Overnight increase in CAVI (ΔCAVI ≥ 0) was observed in 42 patients (70%). The ΔCAVI ≥ 0 group was likely to have moderate-to-severe sleep-disordered breathing (i.e., apnea-hypopnea index ≥15, 55.6% vs 80.9%, P = 0.047) and greater obstructive respiratory events (29.4% vs 58.5%, P = 0.041). In multivariable analysis, moderate-to-severe sleep-disordered breathing and greater obstructive respiratory events were independently correlated with an overnight increase in CAVI (P = 0.033 and P = 0.042, respectively). In patients hospitalized for acute heart failure, arterial stiffness, as assessed by CAVI, significantly increased overnight. Moderate-to-severe sleep-disordered breathing and obstructive respiratory events may play an important role in the overnight increase in cardio-ankle vascular index.

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis.

Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individual recordings and tested the optimized model on 1000 separate hold-out recordings. F1 scores for the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0.65, 0.61, and 0.60 for the optimized single-event models. Index values computed from detected events correlated positively with manual annotations (r2 = 0.73, r2 = 0.77, r2 = 0.78, respectively). We furthermore quantified model accuracy based on temporal difference metrics, which improved overall by using the joint model compared to single-event models. Our automatic model jointly detects arousals, leg movements and sleep disordered breathing events with high correlation with human annotations. Finally, we benchmark against previous state-of-the-art multi-event detection models and found an overall increase in F1 score with our proposed model despite a 97.5% reduction in model size. Source code for training and inference is available at https://github.com/neergaard/msed.git.

The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing.

Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals. We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI. Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI). Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.

0428 Area Deprivation Index, Sleep Disordered Breathing and Incident Major Cardiovascular Events and Mortality in a Large Cohort

Abstract Introduction Epidemiological studies have shown a strong association between sleep disordered breathing (SDB) and increased incidence of cardiovascular morbidity and mortality, however, to date, the variation of SDB across the socioeconomic spectrum and how this variation is associated with major cardiovascular events (MACE) and mortality is unknown. Methods A retrospective cohort study was conducted using the Cleveland Clinic Sleep Laboratory Registry and included MACE-naïve adults who underwent polysomnography or home sleep apnea test. Area Deprivation Index (ADI), a biomarker of neighborhood socioeconomic disadvantage, was calculated by national rank, i.e. 25th, 50th and 75th percentiles; higher quartiles reflecting greater deprivation (ADI-Q1,2,3,4). ANOVA with pairwise testing was used to compare variables across ADI quartiles. Composite of MACE included heart failure, stroke, atrial fibrillation and myocardial Infarction or death. Cox proportional hazard regression models tested the association between ADI and SDB-measures of apnea hypopnea Index (AHI) and sleep-related hypoxia (total sleep time spent&amp;lt; 90%SaO2, T90) with MACE. Covariates included demographics, medications, smoking status, Elixhauser Comorbidity Score, and comorbidities. Censoring was sleep study date or last encounter in the electronic medical record. Asterisk (*) indicates statistical significance after pairwise testing. Interaction testing used competing risk (Fine &amp; Gray method). Results Of 72,443 patients, those living in ADI-Q4, compared to those living in ADI-Q1, were more likely to be younger (*48.9± 3.9 vs 50.4±14.2), female (*59.9% vs 40.1%), African American (*49% vs 4.9%), with lower AHI (*13.5[5.4, 34.1]vs15.1[6.2,33.1]) and lower degree of hypoxia (T90:*2.9[0.40,19.01] vs 4.4[0.50, 25.6]). Model adjusted risk for MACE or death for those in ADI-Q4 was 79% greater than ADI-Q1 (HR=1.79,95%CI:1.51-2.12,p&amp;lt; 0.0001). Likewise, increased AHI severity and T90 above median were associated with 13% and 21% increased risk of MACE or death (HR=1.13,95%CI:1.07-1.19,p&amp;lt; 0.0001; HR=1.21,95%CI:1.51-1.27,p&amp;lt; 0.001) respectively. Interactions were observed between T90 and ADI with the risk of MACE (p=0.0016) or death (p=0.0051). Conclusion Living in areas of increasing socioeconomic deprivation confers an increased risk for MACE or death. Moreover, sleep-related hypoxia seems to modify this association. ADI appears to be useful to identify populations at risk for worse cardiovascular outcomes and to inform risk stratification for targeted interventions to decreased sleep-health disparities. Support (if any)

1004 A novel use of adaptive servo ventilation for VNS-induced central sleep apnea in an adolescent with refractory epilepsy

Abstract Introduction Epilepsy is frequently encountered in the pediatric population and some patients are treated by stimulation of the vagus nerve to reduce their seizure frequency. Vagus nerve stimulation (VNS) may induce central sleep apnea. Adaptive servo ventilation is a known treatment option for central sleep apnea in certain adult patients with heart failure and Cheyne-Stokes respiration but is infrequently utilized in the pediatric population. Report of case(s) A 17-year-old male with a history of intractable generalized epilepsy, VNS placement, obstructive sleep apnea (OSA) and central sleep apnea (CSA) presented to our tertiary care center for management of his sleep disordered breathing. Initial management of sleep disordered breathing included CPAP for OSA. After VNS placement, the patient began to have a high residual apnea-hypopnea index on data card download of his CPAP. Polysomnography (PSG) was performed on CPAP and CSA events were noted that correlated with VNS firing. BiPAP was titrated and was unsuccessful in treating the CSA. VNS adjustment did not impact the sleep disordered breathing. Repeat titration with adaptive servo ventilation successfully treated the OSA and CSA. In follow up, the patient is tolerating ASV with improvement in his nocturnal sleep disordered breathing events and his daytime functioning. Conclusion Central sleep apnea can be induced by vagus nerve stimulation. This unique case documents successful treatment of vagus nerve stimulation induced central sleep apnea by adaptive servo ventilation in an adolescent patient. Support (if any)

Description of a novel method for detection of sleep-disordered breathing in brachycephalic dogs.

Sleep-disordered breathing (SDB), defined as any difficulty in breathing during sleep, occurs in brachycephalic dogs. Diagnostic methods for SDB in dogs require extensive equipment and laboratory assessment. To evaluate the usability of a portable neckband system for detection of SDB in dogs. We hypothesized that the neckband is a feasible method for evaluation of SDB and that brachycephaly predisposes to SDB. Twenty-four prospectively recruited client-owned dogs: 12 brachycephalic dogs and 12 control dogs of mesocephalic or dolicocephalic breeds. Prospective observational cross-sectional study with convenience sampling. Recording was done over 1 night at each dog's home. The primary outcome measure was the obstructive Respiratory Event Index (OREI), which summarized the rate of obstructive SDB events per hour. Additionally, usability, duration of recording, and snore percentage were documented. Brachycephalic dogs had a significantly higher OREI value (Hodges-Lehmann estimator for median difference = 3.5, 95% confidence interval [CI] 2.2-6.8; P < .001) and snore percentage (Hodges-Lehmann estimator = 34.2, 95% CI 13.6-60.8; P < .001) than controls. A strong positive correlation between OREI and snore percentage was detected in all dogs (rs = .79, P < .001). The neckband system was easy to use. Brachycephaly is associated with SDB. The neckband system is a feasible way of characterizing SDB in dogs.

Effects of continuous positive airway pressure on very short-term blood pressure variability associated with sleep-disordered breathing by pulse transit time-based blood pressure measurements.

Blood pressure (BP) variability (BPV) is a predictor of cardiovascular outcomes independently of BP absolute values. We previously reported that pulse transit time (PTT) enables monitoring beat-to-beat BP, identifying a strong relationship between the extent of very short-term BPV and the severity of sleep-disordered breathing (SDB). Here, we investigated the effects of continuous positive airway pressure (CPAP) on very short-term BPV. We studied 66 patients (mean age 62 years old, 73% male) with newly diagnosed SDB who underwent full polysomnography on two consecutive days for diagnosis (baseline) and CPAP, together with PTT-driven BP continuous recording. PTT index was defined as the average number of acute transient rises in BP (≥12 mmHg) within 30 s/h. CPAP treatment effectively improved SDB parameters, and attenuated PTT-based BP absolute values during the night-time. Very short-term BPV that includes PTT index and standard deviation (SD) of systolic PTT-BP was significantly reduced by CPAP therapy. The changes in PTT index from baseline to CPAP were positively correlated with the changes in apnea-hypopnea index, obstructive apnea index (OAI), oxygen desaturation index, minimal SpO 2 , and mean SpO 2 . Multivariate regression analysis revealed that changes in OAI and minimal SpO 2 , as well as heart failure, were the independent factors in determining the reduction of PTT index following CPAP. PTT-driven BP monitoring discovered the favorable effects of CPAP on very short-term BPV associated with SDB events. Targeting very short-term BPV may be a novel approach to identifying individuals who experience greater benefits from CPAP.

Association of cortical arousals with sleep-disordered breathing events.

The American Academy of Sleep Medicine recommends scoring hypopneas in adults when there is a ≥ 3% oxygen desaturation or when the event is associated with an arousal. However, there is no rule regarding the duration of the interval between the event termination and the associated arousal. The purpose of this study is to explore the timing between arousals and sleep-disordered breathing (SDB) events. We analyzed cortical arousals (> 1.6 million) and SDB events (> 350,000 apneas and > 1.9 million hypopneas) from 11,400 manually scored polysomnography recordings. Only arousals that started within ±30 seconds from the end of SDB events were included. We used the 2 local minimums on either side of the arousal distribution as the start/end times for the distribution and to define which arousals are associated with SDB events. Finally, we calculated arousal probability near the end of SDB events. Cortical arousals with start times that fell within the 2 minimums were considered to be associated with SDB events. Using this definition, we found that 90% of apnea-associated arousals started no earlier than 4 seconds before and no later than 9 seconds after the end of apneas. Similarly, 90% of hypopnea-associated arousals started no earlier than 6 seconds before and no later than 14 seconds after the end of hypopneas, with the peak of the distribution coinciding with event end time. Arousal probability was highest during the first 10 seconds after the end of the event and was higher for longer events. Our results suggest that 90% of SDB-associated arousals start no earlier than 6 seconds before and no later than 14 seconds after the end of the respiratory events. Zitting K-M, Lockyer BJ, Azarbarzin A, etal. Association of cortical arousals with sleep-disordered breathing events. J Clin Sleep Med. 2023;19(5):899-912.

Abstract 9881: Effects of CPAP on Very Short-Term Blood Pressure Variability Associated With Sleep-Disordered Breathing Determined by Pulse-Transit-Time-Based Blood Pressure Measurements

Background: Sleep-disordered breathing (SDB) and blood pressure (BP) variability are closely associated with cardiovascular diseases. We have recently reported that pulse-transit-time (PTT) has enabled to monitor the beat-to-beat BP, identifying a strong relationship between the severity of SDB and the extent to very short-term BP variability. Here, we investigated the effects of continuous positive airway pressure (CPAP) on very short-term BP variability. Methods: We studied 57 patients (mean age 61 years old, male 42) with SDB who underwent the full polysomnography on two consecutive days for diagnosis (baseline) and CPAP. PTT was continuously monitored together, and PTT-based BP values were measured on a beat-to-beat basis. PTT index was defined as the average number of acute transient rises in BP (≥12 mmHg) within 30 seconds per hour. Results: Apnea-hypopnea index (AHI) was decreased from 46.5±22.4 to 13.7±15.0 (P&lt;0.01) by CPAP. CPAP significantly lowered the average value of systolic PTT-based BP from 121±21 to 118±20 mmHg (P=0.04). Importantly, CPAP significantly reduced the PTT index from 31.4±20.1 to 20.8±15.6 (P&lt;0.01) as well as the standard deviation (SD) of systolic PTT-BP from 5.3±1.9 to 4.9±1.4 mmHg (P=0.01), suggesting that very short-term BP variability was improved by CPAP ( Figure ). The changes in PTT index after CPAP were significantly correlated with the improvement in AHI (R=0.55), oxygen desaturation index (R=0.36), minimal SpO 2 (R=0.48), mean SpO 2 (R=0.46), and arousal index (R=0.58). In multivariate linear regression analysis, the change in AHI was the independent factor to determine the reduction of the PTT index after CPAP. Conclusion: PTT-based BP discovered the improvement in very short-term BP variability associated with SDB events by CPAP therapy.

TRANSVENOUS PHRENIC NERVE STIMULATION EFFECTIVELY TREATS CENTRAL SLEEP APNEA DURING BOTH RAPID AND NONRAPID EYE MOVEMENT SLEEP

TRANSVENOUS PHRENIC NERVE STIMULATION EFFECTIVELY TREATS CENTRAL SLEEP APNEA DURING BOTH RAPID AND NONRAPID EYE MOVEMENT SLEEP

Fetal heart rate responses to maternal sleep-disordered breathing

Maternal sleep-disordered breathing is associated with adverse pregnancy outcomes and is considered to be deleterious to the developing fetus. Maternal obesity potentiates sleep-disordered breathing, which, in turn, may contribute to the effect of maternal obesity on adverse fetal outcomes. However, only a few empirical studies have evaluated the contemporaneous effects of maternal sleep-disordered breathing events on fetal well-being. These events include apnea and hypopnea with accompanying desaturations in oxyhemoglobin. This study aimed to reconcile contradictory findings on the associations between maternal apnea or hypopnea events and clinical indicators of fetal compromise. It also sought to broaden the knowledge base by examining the fetal heart rate and heart rate variability before, during, and after episodes of maternal apnea or hypopnea. To accomplish this, we employed overnight polysomnography, the gold standard for ascertaining maternal sleep-disordered breathing, and synchronized it with continuous fetal electrocardiography. A total of 84 pregnant women with obesity (body mass index >30 kg/m2) participated in laboratory-based polysomnography with digitized fetal electrocardiography recordings during or near 36 weeks of gestation. Sleep was recorded, on average, for 7 hours. Decelerations in fetal heart rate were identified. Fetal heart rate and heart rate variability were quantified before, during, and after each apnea or hypopnea event. Event-level intensity (desaturation magnitude, duration, and nadir O2 saturation level) and person-level characteristics based on the full overnight recording (apnea-hypopnea index, mean O2 saturation, and O2 saturation variability) were analyzed as potential moderators using linear mixed effects models. A total of 2936 sleep-disordered breathing events were identified, distributed among all but 2 participants. On average, participants exhibited 8.7 episodes of apnea or hypopnea per hour (mean desaturation duration, 19.1 seconds; mean O2 saturation nadir, 86.6% per episode); nearly half (n=39) of the participants met the criteria for obstructive sleep apnea. Only 45 of 2936 apnea or hypopnea events were followed by decelerations (1.5%). Conversely, most (n=333, 88%) of the 378 observed decelerations, including the prolonged ones, did not follow an apnea or a hypopnea event. Maternal sleep-disordered breathing burden, body mass index, and fetal sex were unrelated to the number of decelerations. Fetal heart rate variability increased during events of maternal apnea or hypopnea but returned to initial levels soon thereafter. There was a dose-response association between the size of the increase in fetal heart rate variability and the maternal apnea-hypopnea index, event duration, and desaturation depth. Longer desaturations were associated with a decreased likelihood of the variability returning to baseline levels after the event. The mean fetal heart rate did not change during episodes of maternal apnea or hypopnea. Episodes of maternal sleep apnea and hypopnea did not evoke decelerations in the fetal heart rate despite the predisposing risk factors that accompany maternal obesity. The significance of the modest transitory increase in fetal heart rate variability in response to apnea and hypopnea episodes is not clear but may reflect compensatory, delimited autonomic responses to momentarily adverse conditions. This study found no evidence that episodes of maternal sleep-disordered breathing pose an immediate threat, as reflected in fetal heart rate responses, to the near-term fetus.

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study.

Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objectiveof this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals. Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments. Of 129patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor. Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.

472 Sleep Quality in Pregnancy: An Analysis of Cardiopulmonary Coupling in the nuMoM2b Cohort

Abstract Introduction The impact of pregnancy on sleep quality and sleep-breathing is of interest due to concerns of an impact on maternal, intra-uterine and neonatal health. The Sleep Disordered Breathing (SDB) sub-study of the Nulliparous Pregnancy Outcomes Study (NuMoM2b) provided a large cohort of single gestational women who underwent home sleep apnea testing (HSAT) to evaluate for SDB. To evaluate changes in sleep during pregnancy, we utilized publicly available data from http://www.sleepdata.org for cardiopulmonary coupling (CPC) analysis to evaluate SDB, as well as sleep duration and quality. No outcomes data is currently available. Methods Standardized Level 3 HSAT was performed after visit 1 (V1), (6–15 weeks’ gestation) and visit 3 (V3), (22 -31 weeks’ gestation) on 3702 women from the NuMoM2b cohort. CPC-analysis was performed using clinically validated algorithms based on CPC-method using ECG and oxygen saturation data (SpO2) as the input signals. SleepImage Apnea Hypopnea Index (sAHI) was calculated to evaluate for SDB (FDA; 182618). Additional calculations to determine sleep latency, sleep duration, wake after sleep onset, and sleep quality (SQI) were performed. The SQI-metric incorporates measures of sleep stability and instability and is then presented on a scale of 0 – 100 where a higher number is desirable. Results 3,261 &amp; 2,511 participants had data at visits 1 &amp; 3, respectively. A total of 3,012 and 2,332 individuals had ECG data of sufficient quality. The mean age of the analyzed cohort was 27 years old. SDB events were overall low, but significantly increased across visits, sAHI [(1.6 ± 2.5)/hour (V1) vs (2.9 ± 4.1) (V3)], p&amp;lt; 0.001. There was a statistically significant increase in sleep latency [7.4 ± 12.7 (V1) vs 18.7 ± 27.8 (V3)], p&amp;lt; 0.001 and reduction in total sleep time [401.2 ± 85.6 (V1) vs 348.5 ± 79.3 (V3)], p&amp;lt; 0.001. Most notably, there was a &amp;gt;10% reduction in the SQI, indicative of increased unstable, fragmented sleep as pregnancy progressed [72.1 ± 13.8 (V1), 60.5 ± 16.2 (V3)], p &amp;lt; 0.001. Conclusion Using objective measures based on CPC analysis from HSAT derived signals, sleep disordered breathing, sleep duration and sleep quality are all adversely impacted as gestation progresses. Support (if any):

A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is a common and heterogeneous disease characterized by episodic collapse within the upper airways, which leads to reduced ventilation and adverse consequences, including hypoxia, hypercapnia, sleep fragmentation, and long-term effects such as cardiovascular comorbidities. The clinical diagnosis of OSA and its severity classification are often determined based on the apnea–hypopnea index (AHI), defining the number of apneic and hypopnea events per hour of sleep. However, the limitations of the AHI to assess disease severity have necessitated the exploration of other metrics for additional information to reflect the complexity of OSA. Novel parameters such as the hypoxic burden have the potential to better capture the main features of OSA by maximizing the information available from the polysomnogram. These emerging measures have described multidimensional qualities of sleep-disordered breathing events and breathing irregularity and will ultimately result in better management of OSA.

Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO 2 Signals.

In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (SpO 2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.

Obstructive Sleep Apnea Is Associated with Newly Diagnosed Gestational Diabetes Mellitus.

Rationale: Sleep-disordered breathing (SDB) is associated with increased risk of adverse pregnancy outcomes, including gestational diabetes mellitus (GDM). GDM is a significant cause of maternal and infant morbidities. Assessing these risk factors concurrently may facilitate both the identification of women at GDM risk and the initiation of GDM prevention strategies.Objectives: To investigate whether SDB events, including SDB in rapid eye movement (REM) sleep and other sleep parameters, are associated with increased risk of GDM and to evaluate the performance of the models investigating associations between breathing and sleep parameters and GDM risk.Methods: In this case-control study, 46 women with newly diagnosed GDM and 46 healthy control subjects, who were individually matched for age, gestational age, body mass index, race, and parity, completed overnight polysomnographic studies and sleep questionnaires after being screened for GDM during the late-second to mid-third trimesters. Conditional logistic regression analysis was used to identify models investigating associations between risk factors and GDM risk. The Bayesian information criterion (BIC) was employed to compare models; the model with the lowest BIC is preferred.Results: Obstructive sleep apnea (OSA; defined as an apnea-hypopnea index [AHI] >5 events/h) was present in 22% of subjects with GDM and 9% of control subjects (P < 0.001). Women with OSA had a higher GDM risk (odds ratio [OR], 4.71; 95% confidence interval [CI], 1.05-21.04). In individual models, GDM risk was also significantly higher among women with higher overall AHI (events/h OR, 1.81; 95% CI, 1.01-3.27), higher AHI in REM (events/h OR, 2.09; 95% CI, 1.02-4.31), higher oxygen desaturation index greater than or equal to 4% (ODI4; events/h OR, 2.21; 95% CI, 1.03-4.73), and higher Sleep Apnea Symptom Score (OR, 2.72; 95% CI, 1.11-6.69). The percentage of non-REM sleep was significantly associated with decreased risk of GDM (percentage of non-REM sleep OR, 0.88; 95% CI, 0.78-0.99). The BIC supports the conclusion that there is a strong association between AHI in REM and GDM risk compared with the other significant models.Conclusions: SDB events, including REM-related OSA, are linked to increased GDM risk. GDM risk is also influenced by intercorrelated sleep variables.

847Interim analysis of the device-detected cardiac tachyarrhythmic events and sleep-disordered breathing (DEDiCATES)" study; focused on baseline characteristics

Abstract Funding Acknowledgements This work is supported by Boston Scientific. Background/introduction: Sleep-disordered breathing (SDB) is one of the common comorbidities in cardiac implantable electronic devices (CIED) patients. It’s feasible to detect SDB using specialized diagnostic function of CIEDs showing comparable performance in diagnostic accuracy of SDB to conventional polysomnography. To date, few studies have investigated the prognostic value of CIED-detected SDB in cardiovascular risk. Purpose In the Device-Detected CArdiac Tachyarrhythmic events and Sleep-disordered breathing (DEDiCATES) study, we aim to determine whether device-detected sleep-disordered breathing (SDB) events are associated with increased risk of cardiac arrhythmias or other cardiovascular (CV) outcomes. Here, we present interim baseline data of the study population to investigate the baseline prevalence and severity of device-detected SDB in CIED patients. Methods The study was designed as a prospective, multicenter, and observational study to include 600 patients. Eligible patients have dual chamber CIEDs with SDB detection function which reports the average number of sleep disturbance events per hour per night in the form of a Respiratory Disturbance Index (RDI). The daily RDI values were collected to measure the severity and burden of SDB. The baseline severity of SDB was determined according to RDI data recorded during the first 1 week, 1 month, and 3 ± 1 months after CIED implantation; patients with RDI≥ 30/h for at least one night are classified into the severe SDB group. Results A total of 313 patients who completed the first device interrogation at 3 ± 1 months were included for analysis. The mean age and CHA2DS2VASc score were 69 and 3.1, respectively. Forty-nine percent of patients were men, and 35.5% were classified as high risk for SDB by Berlin questionnaire. The rate of severe SDB increased with longer monitoring period (83.8% at 1week, 91.6% at 1 month, and 96.4% at 3 ± 1 months). There was no baseline demographic difference between the severe and non-severe SDB groups, except that baseline body mass index (BMI) was higher in the severe group (24.5% vs. 21.5%, p = 0.01). The mean RDI was 36.3, 34.2, and 33.0 per night during the first 1 week, 1 month, and 3 ± 1 months, respectively. Considering the limitation of the categorical criteria of baseline severity, we arbitrarily reclassified the patients according to median mean RDI value. Men were more frequent and BMI was higher in the above-mean RDI group compared to the below-mean RDI group. Using logistic regression analysis, we identified that BMI and male gender were associated with patients with above-mean RDI. Conclusions Conventional categorical classification of SDB severity seems to be limited in its ability to discriminate high risk patients. Thus, more suitable RDI-related variables need to be identified in future outcome analysis. Abstract Figure. Prevalence of baseline severe SDB

Longer duration electroencephalogram arousals have a better relationship with impaired vigilance and health status in obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is a prevalent sleep disorder with significant health consequences. Sleep fragmentation is a feature of OSA and is often determined by the arousal index (ArI), a metric based on the electroencephalograph (EEG). The ArI has a weak correlation with neurocognitive outcomes in OSA patients. In this study, we examine whether changing from the current minimum EEG arousal duration of 3s improves the association between sleep fragmentation and neurocognitive outcomes. In a retrospective study, we selected OSA patients without any other comorbidities that are associated with neurocognitive impairment. The OSA patients were clustered into two groups based on their psychomotor vigilance task (PVT) performance to represent impaired and unimpaired neurocognition. While no differences were found in demographics or usual sleep study statistics, the impaired group had a greater number of EEG arousals greater than 5s (P= 0.034), 7s (P= 0.041), and 15s (P= 0.036) in duration. There were no differences in the number of EEG arousals associated with sleep-disordered breathing events. These differences also corresponded with quality of life outcomes between the two groups. An ArI with a duration of 5s or greater had the best combination of sensitivity (70.0%) and specificity (66.7%) compared with the usual 3s duration (sensitivity and specificity of 70.0% and 53.3%, respectively). A re-examination of the EEG arousal scoring rules, and their duration, may help with allocation of health resources to OSA patients most in need.

1208 Sleep Stage Prediction And Sleep Disordered Breathing Detection Using Raw Actigraphy And Photoplethysmography From Wearable Consumer Device

Abstract Introduction Wearable, multisensory consumer devices that estimate sleep are prevalent and hold great potential. Most validated actigraphic prediction studies of sleep stages (SS) have only used low resolution (30 sec) data and the Cole-Kripke algorithm. Other algorithms are often proprietary and not accessible or validated. We present an automatic, data-driven deep learning algorithm that process raw actigraphy (ACC) and photoplethysmography (PPG) using a low-cost consumer device at high (25Hz) and low resolution to predict SS and to detect sleep disordered breathing (SDB) events. Methods Our automatic, data-driven algorithm is a deep neural network trained and evaluated to predict SS and SDB events on 236 recordings of ACC data from a wrist-worn accelerometer and PPG data from the overlapping PSG. The network was tested on raw ACC and PPG data, which was collected at 25 Hz using the HUAMI Arc2 wristband from 39 participants that underwent a nocturnal polysomnography (PSG). Results Overall accuracy (Acc), recall (Re), specificity (Sp), and kappa (κ) per subject on the test dataset the prediction of wake, NREM, REM was Acc=76.6%, Re=72.4%, Sp=78.0%, kappa=0.42. On average, we found a 7 % higher performance using the raw sensor data as input instead of processed, low resolution inputs. PPG was especially useful for REM detection. The network assigned 55.6% of patients to the correct SDB severity group when using an apnea-hypopnea index above 15. Conclusion Current results show that SS prediction is significantly improved when using the raw sensor data; it indicates that the system holds promise as a potential pervasive monitoring device for patients with chronic sleep disorders. In contrast the system did not show potential as a sleep apnea screening tool. Additional studies are ongoing to examine the effects of pathology such as sleep apnea and periodic leg movement on SS prediction. Support Technical University of Denmark; University of Copenhagen, Copenhagen Center for Health Technology, Klarman Family Foundation.