Respiratory Effort-related Arousals Research Articles

Are scoring respiratory effort-related arousals worth the effort? --A study comparing outcomes between 4 % vs 3 % hypopnea scoring rules

Study objectivesThe respiratory effort-related arousal (RERA) has been combined with apneas and hypopneas into the respiratory disturbance index (RDI). RERAs are characterized by ≥ 10 s of increasing upper airway effort terminating in arousal without meeting hypopnea criteria. The recent change to hypopnea definitions now includes a ≥30 % reduction in airflow for 10 s with EITHER a 3 % oxygen desaturation OR an arousal. Consequently, many events previously categorized as RERAs will now be included in the 3 % hypopneas, likely reducing the number of events scored as RERAs. We hypothesized that the 3 % apnea-hypopnea index (3%AHI) would approximate the 4%RDI, with the number of 3 % RERAs being negligible. Research questionHow does the transition from the 4 % to the 3 % hypopnea rules impact the significance of RERAs in clinical practice, and how we should relate the AHI and RDI using the different hypopnea rules? MethodsWe prospectively collected 76 consecutive polysomnography results in 4 adult age groups. We re-scored the respiratory events utilizing both the 3 % and the 4 % hypopnea rules and compared the outcomes. ResultsAmong 76 diagnostic studies (mean age 47.5 years, males 47.4 %), the 3 % RERA index [0.8 (0.0, 3.1)] [median (Q1, Q3)] was significantly lower than the 4 % RERA index [3.5 (1.0, 7.3)]. The 3%AHI was 3.07 ± 9.23 (mean ± SD) higher than the 4%RDI (p = 0.005). The 3%AHI was 8.63 ± 8.86 higher than the 4%AHI in all age groups (p < 0.001). This was mainly due to an increased hypopnea index (+8.51 ± 9.03, p < 0.001). In patients with obstructive sleep apnea (OSA), the 3%RERA contributes 4.3 % to the 3%RDI, while the 4%RERA contributes 27.7 % to the 4%RDI. InterpretationsBoth 3%RDI and 3%AHI are higher than the 4%RDI, primarily due to identification of more hypopnea events, resulting in more patients being classified as having OSA. This change in criteria complicates the comparison of hypopnea and RERA contributions between sleep studies scored using the different hypopnea rules.

Differences in Polysomnographic and Craniofacial Characteristics of Catathrenia Phenotypes: A Cluster Analysis.

Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy. Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed. Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, p=0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm3, p<0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, p=0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], p=0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm2, p<0.001) and better response to treatment (41.2% and 82.6%, p=0.004). Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.

Types of Epiglottic Collapse in Breathing Sleep Disorder and Their Impact in Clinical Practice.

Introduction Obstructive sleep apnea (OSA) is a severe form of sleep-disordered breathing (SDB) that is strongly correlated with comorbidities, in which epiglottic collapse (EC) and other contributing factors are involved. Objectives To evaluate the occurrence of EC in OSA patients through drug-induced sleep endoscopy (DISE) and to determine the factors contributing to EC. Methods A retrospective study of 37 adult patients using medical history. Patients were assessed for laryngopharyngeal reflux (LPR) and lingual tonsil hypertrophy (LTH) using reflux symptom index and reflux finding score (RFS); for OSA using polysomnography, and for airway collapse through DISE. An independent t -test was performed to evaluate risk factors, including the involvement of three other airway structures. Results Most EC patients exhibited trap door epiglottic collapse (TDEC) (56.8%) or pushed epiglottic collapse (PEC) (29.7%). Lingual tonsil hypertrophy, RFS, and respiratory effort-related arousal (RERA) were associated with epiglottic subtypes. Laryngopharyngeal reflux patients confirmed by RFS (t(25) = -1.32, p = 0.197) tended to suffer PEC; LTH was significantly associated (X2(1) = 2.5, p = 0.012) with PEC (odds ratio [OR] value = 44) in grades II and III LTH patients; 11 of 16 TDEC patients had grade I LTH. Pushed epiglottic collapse was more prevalent among multilevel airway obstruction patients. A single additional collapse site was found only in TDEC patients. Conclusion Laryngopharyngeal reflux causes repetitive acid stress toward lingual tonsils causing LTH, resulting in PEC with grade II or III LTH. Trap door epiglottic collapse requires one additional structural collapse, while at least two additional collapse sites were necessary to develop PEC. Respiratory effort-related arousal values may indicate EC.

Correlation Between Type 2 Diabetes Mellitus with the Risk of Obstructive Sleep Apnea in Prof. Dr. I.G.N.G. Ngoerah Hospital Denpasar

Background: Obstructive Sleep Apnea (OSA) is part of a complex sleep disordered breathing syndrome. OSA is defined by periods of respiratory events such as apneas, hypopneas, or respiratory effort related arousals during sleep, leading to hypoxia, waking during the night, excessive daytime sleepiness and hindering daily activities. In addition, OSA can also cause various systemic complications and even death. The occurrence of OSA is associated with the presence of metabolic disorders that cause type 2 diabetes mellitus (DM), with as many as 30% patients with OSA also have type 2 DM. The occurence of OSA are often underdiagnosed, especially in high-risk population. Therefore, the purpose of this study is to find out the relationship between type 2 DM with the risk of OSA. Methods: A cross sectional analytic study was conducted from June to August 2022 at Prof. Dr. I.G.N.G. Ngoerah Hospital Denpasar with a total of 80 respondents. The risk of OSA in the respondents was recorded using the Berlin questionnaire. Results: Of the 80 respondents, there were 33 people with a high risk of OSA and 47 people with a low risk of OSA. Respondents with a high risk of OSA were mostly male (78.6%), with the most ages between 51-60 years (53.6%), had type 2 DM (78.6%), and with nutritional status obese I (60.7%). There is a significant relationship between OSA and type 2 DM with a p-value of 0.019 (&lt;0.05), a prevalence ratio of 3.3 with a 95% confidence interval (1.2-9.0), and a correlation coefficient of 0.254. Conclusion: There is a significant relationship between type 2 DM and OSA and is positively correlated. Type 2 DM is a risk factor of OSA, with 3.3 times the risk to develop a high risk OSA compared to patients without type 2 DM.

1018 Finally Awake: Treatment of Severe REM-Isolated Obstructive Sleep Apnea

Abstract Introduction Obstructive sleep apnea (OSA) is typically treated with positive airway pressure (PAP) therapy in symptomatic patients with ≥ 5 obstructive respiratory events (apneas, hypopneas or respiratory effort related arousals) per hour of sleep. In this case, we present a patient with severe OSA isolated to REM sleep who demonstrated significant symptomatic improvement on PAP therapy, despite an overall apnea-hypopnea index (AHI) of less than 5 events/hour. Report of case(s) A 73-year-old obese male with history of SVT, hypertension, and diabetes presented with symptoms of snoring, witnessed apneas, non-restorative sleep, restless sleep, excessive daytime sleepiness, and fatigue. A baseline polysomnogram (PSG) demonstrated severe OSA exclusively in REM sleep (overall AHI 2.9, REM AHI of 37.3, minimum SpO2 of 84%). A subsequent PAP titration study tested CPAP 5-17 and BPAP 19/15 cm of water but was unable to identify an effective pressure during REM sleep. Pressures as low as CPAP 5 cm of water appeared effective in NREM sleep. Ultimately, as the patient was unwilling to undergo a re-titration study, empiric treatment with auto-BPAP (min EPAP 5, max IPAP 25, PS 4) was initiated. With excellent adherence to BPAP therapy, the patient experienced profound symptomatic benefit, with resolution of daytime symptoms, less restlessness during sleep, and more restorative sleep. Conclusion This case highlights a gap, and potential pitfall, in current practice. Our patient 1) did not meet the overall AHI criteria for diagnosis of OSA yet had typical symptoms and high pretest probability for OSA; 2) had a PSG which showed severe OSA exclusively during REM sleep; and 3) experienced resolution of symptoms with PAP therapy. Presently, there is no consensus on whether REM-dependent OSA should be treated if the overall AHI is &amp;lt; 5 events/hour. In fact, there is no standardized definition for REM-dependent OSA. This patient with cardiovascular risk factors was able to receive PAP therapy through the Veterans’ Affairs, and access to PAP therapy changed the Veteran’s quality of life dramatically. Obtaining a PAP device for this patient through private payors would have been nearly impossible. More research is needed to understand the clinical implications of this condition. Support (if any)

Evaluation of Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio in Obstructive Sleep Apnea Patients

Background: Obstructive sleep apnea (OSA) is a condition marked by recurrent total or partial collapses of the upper airway during sleep, which result in obstructive apneas, hypopneas, and/or respiratory effort-related arousals.

Upper Airway Resistance Syndrome : An Underdiagnosed Sleep-related Breathing Disorder

Upper Airway Resistance Syndrome (UARS) is an example of sleep-related breathing disorders. UARS was first reported by Guilleminault in 1992 and it is known as excessive daytime sleepiness without obvious apnea or hypopnea that is characterized by more than 50% of respiratory effort related arousals (RERAs) during sleep. The prevalence of UARS was 15% in population in São Paulo. Although clinical manifestations, electroencephalogram (EEG) findings, and pathogenesis differ between UARS and OSA (Obstructive Sleep Apnea), it still sparked a debate among researchers as to whether UARS should be categorized as a distinct disorder. The International Classification of Sleep Disorders - Third Edition (ICSD3) integrated UARS into OSA diagnosis, resulting in underdiagnosed and undertreated patients with sleep-related breathing disorders who did not necessarily meet OSA criteria. Untreated UARS, on the other hand, has a variety of clinical consequences, including poor weight and height growth, daytime irritability, worsening of quality of life, and cardiovascular complications. Continuous positive airway pressure therapy, oral appliances, surgical management, weight reduction, and positional therapy are all considered as options in management of UARS.&#x0D;

Sleep-Related Head Jerk Presenting With Dream Enactment Behavior: A Case Report

Sleep-related head jerk (SRHJ) is conceived as a physiological motor phenomenon, which is mostly seen during rapid eye movement (REM) sleep. It should be distinguished from movements during REM sleep, mainly REM sleep behavior disorder (RBD). A 25-year-old male complained of daytime sleepiness with snoring and dream enactment behavior (DEB). Video polysomnography (PSG) excluded RBD and obstructive sleep apnea; frequent SRHJs during REM sleep with or without respiratory effort-related arousals, and SRHJs followed by one episode of DEB was observed. On PSG with continuous positive airway pressure titration, SRHJ with arousal events still remained but DEB symptom was resolved. REM without atonia unrelated to head jerks was not observed on all PSG data. Excessive SRHJs with arousal related to a sleep disturbance could indicate an SRHJ disorder, which might be a novel sleep-related movement disorder. In addition, clinicians should be careful in diagnosing young patients with RBD.

Automated Scoring of Respiratory Events in Sleep With a Single Effort Belt and Deep Neural Networks.

Automatic detection and analysis of respiratory events in sleep using a single respiratoryeffort belt and deep learning. Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings. For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.41 ± 7.8 and a r2 of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas. Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation. The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.

0736 Mandibular Movements Are a Reliable Noninvasive Alternative to Esophageal Pressure for Measuring Respiratory Effort in Patients with Sleep Apnea Syndrome

Abstract Introduction Differentiation between obstructive and central apneas and hypopneas requires quantitative measurement of respiratory effort (RE) using esophageal pressure (PES), which is rarely implemented. This study investigated whether the sleep mandibular movements (MM) signal recorded with a tri-axial chin sensor (Sunrise, Namur, Belgium) is a reliable surrogate of PES in patients with suspected obstructive sleep apnea (OSA). Methods In-laboratory polysomnography (PSG) with PES and concurrent MM monitoring was performed. PSGs were scored manually using AASM 2012 rules. Data blocks (n=8042) were randomly sampled during normal breathing (NB), obstructive or central apnea/hypopnea (OA/OH/CA/CH), respiratory effort-related arousal (RERA), and mixed apnea (MxA). Analyses were: evaluation of the similarity and linear correlation between PES and MM using the longest common subsequence (LCSS) algorithm and Pearson’s coefficient; description of signal amplitudes; estimation of the marginal effect for crossing from NB to a respiratory disturbance for a given change in MM signal using a mixed linear-regression. Results Participants (n=38) had mild to severe OSA (median AH index 28.9/h; median arousal index 23.2/h). MM showed a high level of synchronization with concurrent PES signals. Distribution of gyroscope MM signal amplitude differed significantly between event types: median (95% confidence interval) values of 0.60 (0.17–2.43) for CA, 0.83 (0.23–4.71) for CH, 1.93 (0.54–5.57) for MxA, 3.23 (0.72–18.09) for OH, and 6.42 (0.88–26.81) units for OA. Mixed regression indicated that crossing from NB to central events would decrease gyroscope MM signal amplitude by –1.23 (CH) and –2.04 (CA) units, while obstructive events would increase gyroscope MM signal amplitude by +3.27 (OH) and +6.79 (OA) units (all p&amp;lt;10-6). Conclusion In OSA patients, MM signals facilitated the measurement of specific levels of RE associated with obstructive, central or mixed apneas and/or hypopneas. A high degree of similarity was observed with the PES gold-standard signal. Support (If Any)

Mandibular Movements are a Reliable Noninvasive Alternative to Esophageal Pressure for Measuring Respiratory Effort in Patients with Sleep Apnea Syndrome.

PurposeDifferentiation between obstructive and central apneas and hypopneas requires quantitative measurement of respiratory effort (RE) using esophageal pressure (PES), which is rarely implemented. This study investigated whether the sleep mandibular movements (MM) signal recorded with a tri-axial gyroscopic chin sensor (Sunrise, Namur, Belgium) is a reliable surrogate of PES in patients with suspected obstructive sleep apnea (OSA).Patients and MethodsIn-laboratory polysomnography (PSG) with PES and concurrent MM monitoring was performed. PSGs were scored manually using AASM 2012 rules. Data blocks (n=8042) were randomly sampled during normal breathing (NB), obstructive or central apnea/hypopnea (OA/OH/CA/CH), respiratory effort-related arousal (RERA), and mixed apnea (MxA). Analyses were evaluation of the similarity and linear correlation between PES and MM using the longest common subsequence (LCSS) algorithm and Pearson’s coefficient; description of signal amplitudes; estimation of the marginal effect for crossing from NB to a respiratory disturbance for a given change in MM signal using a mixed linear-regression.ResultsParticipants (n=38) had mild to severe OSA (median AH index 28.9/h; median arousal index 23.2/h). MM showed a high level of synchronization with concurrent PES signals. Distribution of MM amplitude differed significantly between event types: median (95% confidence interval) values of 0.60 (0.16–2.43) for CA, 0.83 (0.23–4.71) for CH, 1.93 (0.46–12.43) for MxA, 3.23 (0.72–18.09) for OH, and 6.42 (0.88–26.81) for OA. Mixed regression indicated that crossing from NB to central events would decrease MM signal amplitude by –1.23 (CH) and –2.04 (CA) units, while obstructive events would increase MM amplitude by +3.27 (OH) and +6.79 (OA) units (all p<10−6).ConclusionIn OSA patients, MM signals facilitated the measurement of specific levels of RE associated with obstructive, central or mixed apneas and/or hypopneas. A high degree of similarity was observed with the PES gold-standard signal.

Quality of life in upper airway resistance syndrome.

To evaluate the health-related quality of life (HRQoL) in patients with upper airway respiratory syndrome (UARS) and obstructive sleep apnea (OSA) compared to the general population (GP) in Lima, Peru, and to explore the variables associated with differences in HRQoL in patients with UARS. This was a retrospective study of medical and polysomnography records from 2009-2014 in a referral sleep medicine center for patients aged 18-64 years. UARS was defined by polysomnography as follows: apnea-hypopnea index < 5 events/h, oxygen saturation ≥ 92%, respiratory effort-related arousal index ≥ 5. HRQoL was assessed using the 36-Item Short Form Survey (version 1) questionnaire validated in Peru. The GP HRQoL was obtained from a population-based survey. Linear and logistic regression analyses were conducted. We reviewed 1,329 polysomnograms and selected 888. UARS and OSA were diagnosed in 93 and 795 participants, respectively. The GP cohort consisted of 641 participants. Total HRQoL mean scores (95% confidence interval) in patients with UARS, patients with OSA, and the GP were 67.4 (63.7-71.1), 66.9 (65.4-68.4), and 82.9 (81.6-84.3), respectively. Patients with UARS and patients with OSA had a 5.5 times (95% confidence interval, 3.3-9.2) and 6.2 times (95% confidence interval, 4.6-8.4) greater probability of having a low total HRQoL score compared to patients in the GP, respectively. In patients with UARS, muscle pain, use of psychotropic medication, obesity, and depression were negatively correlated with the total HRQoL score. The impact of OSA and UARS on HRQoL is similar between disease groups and markedly worse when compared to the impact in the GP. In patients with UARS, the presence of muscle pain, obesity, female sex, depression, and use of psychotropic medication negatively impacted HRQoL. Vizcarra-Escobar D, Duque KR, Barbagelata-Agüero F, Vizcarra JA. Quality of life in upper airway resistance syndrome. J Clin Sleep Med. 2022;18(5):1263-1270.

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics.

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.

Tidal volume and stroke volume changes caused by respiratory events during sleep and their relationship with OSA severity: a pilot study.

Breath-by-breath tidal volume (TV) and beat-by-beat stroke volume (SV) were continuously measured in patients with OSA undergoing polysomnography (PSG). The objectives were to (1) determine the changes in TV/SV in response to respiratory events and (2) assess the relationship between these changes and the disease severity. From the PSG data of nine patients withOSA, six different types of respiratory events were identified, i.e., flow limitation (FL), respiratory effort related arousal (RERA), hypopnea with arousal only (Ha), hypopnea with desaturation only (Hd), hypopnea with arousal and desaturation (Had), and apnea. The measured TV and SV values during and after each respiratory event were compared with the pre-event baseline values. The mean TV/SV reductions during all hypopneas and apneas were 38.1%/4.2% and 70.5%/8.8%, respectively. Among three different hypopnea types, the reductions in TV during Hd and Had were significantly greater than those during Ha. The TV reductions during Ha and FL were similar. After RERA, Ha, Had, and apnea, there was an overshoot in TV and SV values, whereas there was no overshoot after FL and Hd. During RERA, there was no reduction in TV/SV. The changes in TV during and after each type of respiratory event were significantly different in most cases. The changes in SV between hypopnea and apnea were different with statistical significance. The AHI does not properly account for the ventilation losses caused by respiratory events. Thus, TV measurements might be useful in the future in assessing the OSA severity in conjunction with the AHI.

Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea.

Given the high prevalence and risk for outcomes associated with pediatric obstructive sleep apnea (OSA), there is a need for simplified diagnostic approaches. A prospective study in 140 children undergoing in-laboratory polysomnography (PSG) evaluates the accuracy of a recently developed system (Sunrise) to estimate respiratory efforts by monitoring sleep mandibular movements (MM) for the diagnosis of OSA (Sunrise™). Diagnosis and severity were defined by an obstructive apnea/hypopnea index (OAHI) ≥ 1 (mild), ≥ 5 (moderate), and ≥ 10 events/h (severe). Agreement between PSG and Sunrise™ was assessed by Bland-Altman method comparing respiratory disturbances hourly index (RDI) (obstructive apneas, hypopneas, and respiratory effort-related arousals) during PSG (PSG_RDI), and Sunrise RDI (Sr_RDI). Performance of Sr_RDI was determined via ROC curves evaluating the device sensitivity and specificity at PSG_OAHI ≥ 1, 5, and 15 events/h. A median difference of 1.57 events/h, 95% confidence interval: -2.49 to 8.11 was found between Sr_RDI and PSG_RDI. Areas under the ROC curves of Sr_RDI were 0.75 (interquartile range [IQR]: 0.72-0.78), 0.90 (IQR: 0.86-0.92) and 0.95 (IQR: 0.90-0.99) for detecting children with PSG_OAHI ≥ 1, PSG_OAHI ≥ 5, or PSG_ OAHI ≥ 10, respectively. MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.

The rate of decrease in oxygen desaturation during severe obstructive sleep apnea syndrome is correlated with subjective excessive daytime sleepiness.

To explore factors that influence subjective excessive daytime sleepiness (EDS) in patients with severe obstructive sleep apnea syndrome (OSAS). Patients with snoring seen at the Sleep Medicine Center of The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University between October 2018 and November 2019 were included in this study. All patients underwent polysomnography (PSG). Noninvasive frequency-domain analysis was used to assess the autonomic nervous system regulation of the heart, with the low frequency (LF)/high frequency (HF) power ratio used to represent the sympathetic-parasympathetic balance. Daytime sleepiness was evaluated by the Epworth sleepiness scale (ESS). Overnight apnea episodes were included for analyses. The rate of pulse oxyhemoglobin saturation (SpO2) decrease was measured as the change in the percentage of SpO2 per second after obstructive apnea and was expressed as the oxygen desaturation rate (ODR). A total of 101 patients with severe OSAS were enrolled in this study and were further divided into two groups: the EDS group (ESS > 10, n = 52) and the non-EDS group (ESS ≤ 10, n = 49). The apnea-hypopnea index (AHI), respiratory effort-related arousals (RERAs), and LF/HF power ratio were significantly higher in the EDS group than in the non-EDS group (AHI: 69.9 ± 14.5 vs. 57.9 ± 16.1 events/h; RERAs: 42.2 ± 16.7 vs. 30.4 ± 13.7 events/h; LF/HF power ratio: 2.9 ± 0.8% vs. 2.4 ± 0.9%, all p < 0.001). Multiple linear regression analyses revealed that after adjusting for covariates expected to affect this relationship, ESS scores were correlated with ODR (β = 0.520, p < 0.001) and LF/HF power ratio (β = 0.155, p = 0.028), rather than with the traditional sleep-disordered breathing parameters. Compared with the traditional PSG parameters, both ODR and an increased LF/HF power ratio were more closely related to daytime sleepiness, especially ODR.

Obstructive sleep apnea - risk factors, diagnosis and management

Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is characterised by obstructive apneas, hypopneas and respiratory effort-related arousals caused by repetitive collapse of the upper airway during sleep. Most pauses in breathing last between 10 and 30 seconds, but some may persist longer.

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic detection of sleep arousals is still challenging. In this paper, a novel method is presented to detect non-apnea sources of arousals during sleep using Polysomnography(PSG) recordings. After the preprocessing, a sequence-to-sequence deep neural network (DNNs) consisting of a series of Bidirectional long short-term memory (Bi-LSTM) layer, and fully connected layers were trained to classify samples in the segments. Initially, three different models were prepared for different datasets. Finally, obtaining the classification result through an ensemble model consisting of the three trained models. The result shows that the area under the receiver precision-recall curve (AUPRC) is 0.59 for the test dataset exceeding the performance of the classifiers in the existing literature.Clinical relevance- Analyzing Polysomnographic recordings is a time consuming a critical process yet to identify sleep disorders. These recordings span several hours and contain different data streams that include EEG, EMG, etc. This paper proposes a system that can automatically detect respiratory effort-related arousals using a deep neural network from Polysomnographic Recordings. By automating this process with a machine learning-based solution that can eliminate the manual process while facilitating further improvements of the system with future data.

Sleep-related leg movements in obstructive sleep apnea: definitions, determinants, and clinical consequences

Study objectivesTo investigate (1) the effect of different scoring rules on leg movement (LM) classification in patients with obstructive sleep apnea (OSA); (2) determinants of respiratory event related leg movements (rLM); and (3) to relate LM parameters to clinical outcomes. Methods(1) LM classification was compared between the World Association of Sleep Medicine (WASM) 2006 and the WASM 2016 rules in 336 participants with apnea hypopnea index (AHI) ≥ 5; (2) determinants and features of rLM were investigated with logistic mixed regression in 172 participants with AHI ≥ 10 and respiratory disturbance index (RDI) ≥ 15, and (3) LM parameters were compared for patients with and without cardiovascular events and related to continuous positive airway pressure (CPAP) adherence. ResultsWASM-2016 scoring significantly reduced periodic limb movements of sleep (PLMS) frequency in OSA participants even when only considering the new periodicity criteria. Probability of rLM was strongly increased when respiratory events ended with an arousal, but rLM probability was lower for hypopneas and respiratory effort-related sleep arousal (RERAs) than for obstructive apneas. In participants with frequent non-respiratory PLMS, rLM were more frequent and behaved more PLMS-like. In participants without PLMS, rLM probability mostly depended on respiratory event features. LM parameters were neither related to cardiovascular event risk nor to CPAP-adherence. ConclusionsIt is likely that the PLMS frequency in OSA populations has been previously overestimated. Our results suggest that there are two types of rLM, true periodic ones that happen to synchronize with the respiratory events, and periodic appearing but respiratory driven LM, and that the presence of non-respiratory PLMS is instrumental in distinguishing between the two.

0731 Obstructive Sleep Apnea with Predominant Respiratory Effort Related Arousals: Theta Power in C3- M2 And C4-M1 Derivation Comparison in Male And Female

Abstract Introduction Theta power in electroencephalography has been studied as a correlate to REM sleep. An increase in theta power during REM sleep has been observed in patients during recovery sleep after sleep deprivation. Emotional memories appear to be processed and consolidated during REM sleep.The role of hippocampal theta wave activity during REM sleep on emotional memory processing is limited. The importance of theta power has not been well characterized in patients with obstructive sleep apnea (OSA) with predominant respiratory Effort Related Arousals (RERAs). This report aims to study the theta power in patients with OSA with predominant Respiratory Effort Related Arousals (RERAs) with an apnea-hypopnea index (AHI) of &amp;lt; 5. Methods We have identified 38 patients with baseline polysomnograms performed from December 2019 to July 2019 with AHI &amp;lt; 5 and a Respiratory Disturbance Index (RDI) of at least 5 or greater. Patients with chronic hypoxemic respiratory failure, hypoventilation and predominant central sleep apnea were excluded from the study. Total power of frequency in bands was obtained for theta waves (4-8 Hz) and total waves (1-30 Hz). Relative theta power was calculated on the last REM sleep using C3-M2 and C4-M1 derivations. Paired two-tailed t-Test was performed on the theta power in C3-M2 and C4-M1 in both the sexes. Results Initial analysis was performed in 38 patients out of which 20 were male and 18 female. Among males, (Mean ± SEM) age was 52.3 (±2.9); Epworth Sleepiness Scale (ESS) of 6.6 (±1.1), AHI of 2.1 (±0.3), and RDI of 7.3 (±0.3).Whereas in female (Mean ± SEM) age was 46.8 (± 2.8), ESS of 7.7 (±1.4), AHI of 2.3 (±0.35), and RDI of 6.9 (±0.4). Statistically significant difference was noted in the theta power between the C3-M2 and C4-M1 derivations with P value of 0.03 and 0.04 in male and female respectively. However, no significant difference was found when C3-M2 and C4 -M1 was compared between male and female. Further, statistical analysis will be performed after gathering data from a larger sample size. Conclusion There was significant difference between C3-M2 and C4-M1;overall no difference was found between sexes. Support none