Breathing Events Research Articles

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.

Selective serotonin reuptake inhibitor use is associated with worse sleep-related breathing disturbances in individuals with depressive disorders and sleep complaints: a retrospective study.

The effects of serotonergic agents on respiration neuromodulation may vary according to differences in the serotonin system, such as those linked to depression. This study investigated how sleep-related respiratory disturbances relate to depression and the use of medications commonly prescribed for depression. Retrospective polysomnography was collated for all 363 individuals who met selection criteria out of 2,528 consecutive individuals referred to a specialized sleep clinic (Ottawa, Canada) between 2006 and 2016. The apnea-hypopnea index (AHI), oxygen saturation nadir, and oxygen desaturation index during REM and NREM sleep were analyzed using mixed analyses of covariance comparing 3 main groups: (1) medicated individuals with depressive disorders (antidepressant group; subdivided into the selective serotonin reuptake inhibitor and norepinephrine-dopamine reuptake inhibitor subgroups), (2) non-medicated individuals with depressive disorders (non-medicated group), and (3) mentally healthy control patients (control group). Individuals with depressive disorders (on antidepressants or not) had significantly higher AHIs compared to control patients (both P ≤ .007). The antidepressant group had a lower NREM sleep oxygen saturation nadir and a higher NREM sleep oxygen desaturation index than the control and non-medicated groups (all P ≤ .009). Within individuals with depressive disorders, independent of depression severity, the selective serotonin reuptake inhibitor group had a lower oxygen saturation nadir and a higher oxygen desaturation index during NREM sleep than the norepinephrine-dopamine reuptake inhibitor (both P ≤ .045) and non-medicated groups (both P < .001) and a higher NREM sleep AHI than the non-medicated group (P = .014). These findings suggest that the use of selective serotonin reuptake inhibitors may be associated with impaired breathing and worse nocturnal oxygen saturation in individuals with depressive disorders and sleep complaints, but this needs to be confirmed by prospective studies.

Euthyroid Sick Syndrome in Patients With COVID-19.

BackgroundCoronavirus disease 2019 (COVID-19) has been shown to affect almost every organ throughout the body. However, it is not clear whether the thyroid gland is impaired in COVID-19 patients. Euthyroid sick syndrome (ESS) is usually associated with the disease severity and deterioration prognosis in critical illness. In this study, the thyroid function of COVID-19 patients was assessed and factors associated with outcomes were analyzed to determine the potential predictive value of ESS.MethodsClinical and laboratory data of COVID-19 patients with or without ESS in Changsha, China, were collected and analyzed on admission. Kaplan-Meier curve and cox regression model were utilized to determine the correlation between ESS and the endpoints. Subsequently, a receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performances of FT3 and C-reactive protein (CRP) in the disease severity.ResultsForty-one (27.52%) cases of COVID-19 patients diagnosed with ESS. ESS patients had higher proportions of fever, shortness of breath, hypertension, diabetes, and severe events than those of non-ESS patients. The levels of erythrocyte sedimentation rate and C-reactive protein, and the positive rate of procalcitonin were significantly higher, whereas the lymphocyte count was apparently lower in ESS patients than in non-ESS patients. The regression analysis showed that ESS was significantly associated with the disease severity of COVID-19 (HR = 2.515, 95% CI: 1.050–6.026, P = 0.039). The areas under the curve (AUCs) for predicting the severe disease were [0.809 (95% CI 0.727–0.892), P < 0.001] and [0.792 (95% CI 0.689–0.895), P < 0.001] for FT3 and CRP, respectively.ConclusionESS was significantly associated with the disease severity and inflammatory parameters in COVID-19 patients.

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.

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.

0584 Detecting Sleep Disordered Breathing Using Sub-Terahertz Radio-Frequency Micro-Radar

Abstract Introduction New sensor technologies are entering sleep testing at a rapid pace; Neteera™ developed a novel sensor and algorithm for sleep apnea detection utilizing a contact-free, radar-based sensor system. The system utilizes a high-frequency, low-power, directional micro-radar which operates at ~120GHz and a sampling rate of 2500Hz as well as algorithms which are able to detect both pulse and respiratory activity of subjects during sleep. Methods Adult subjects undergoing diagnostic PSG for clinical purposes were simultaneously assessed with the novel micro-radar system with sensors under the mattress. Disordered breathing events (DBEs) were scored from the PSG using AASM scoring guidelines and were compared with those detected by the micro-radar sensor. Test data were grouped into three sets: 1. Single under mattress sensor; 2. Two under mattress sensors on each side of the bed (to improve signal capture); 3. After software optimization. The micro-radar sensor detected DBEs but software to describe the type of DBEs (obstructive apnea/central apnea/hypopnea) is still under development. Detection rate of DBEs was compared between the two methodologies and the development sets. Results n=22 (12 F, 10 M), Age=50.8±12.4 years, BMI=35.32±7.37 kg/m2. Diagnostic PSG AHI: 19.7±29.4/hr, T90=15.8±25.7%. Percent DBEs missed by the micro-radar sensor: 1st set=14.6±10.6%; 2nd set=9.4±8.3%; 3rd set=1.2±2.6%. Number of DBEs assessed for each set was 646, 1144, 125 events, respectively. With each successive set, the detection rate improved. Conclusion A novel micro-radar, non-contact sensor technology can be used to detect DBEs during sleep. Detection rate improved with utilization of two sensors per bed and software optimization. Future software development is expected to improve detection rate and facilitate breathing event classification into obstructive apneas/central apneas/hypopneas. Support None.

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.

0914 Sleep Disordered Breathing In Pediatric Patients Before And After Intrathecal Baclofen Therapy

Abstract Introduction Refractory spasticity in children is treated with intrathecal baclofen (ITB), which may worsen both central and obstructive breathing events. Sleep disordered breathing (SDB) is seldom investigated prior and/or subsequent to placement of ITB and there are currently no standardized protocols. This study aims to compare occurrence of SDB pre/post ITB placement. Methods Retrospective chart review revealed 104 patients started on ITB therapy from 2009-2019 and those who had pre and/or post ITB polysomnograms (PSG) were included. Medical history and PSG parameters were extracted. Comparison of paired results will occur using the Wilcoxon Signed Rank Sum Tests once collection is complete. Results Thirty-seven patients were identified having pre and/or post ITB PSGs. Results in mean ± SD show: age was 11 ± 4 years and 65% were male. Twenty-five pre ITB PSG had an oAHI of 4 ± 5 with 22/25 (88%) having SDB. There were 15/25 (60%) with mild OSA (oAHI &amp;gt;1 but &amp;lt; 5) and 7/25 (28%) with moderate-severe OSA (oAHI &amp;gt; 5/hr). CAI was 1 ± 2 and oxygen saturation nadir was 88 ± 9 %. Sixteen post ITB PSG had an oAHI of 8 ± 13 with 100% having SDB. There were 11/16 (69%) with mild OSA and 5/16 (31%) with moderate-severe OSA. CAI was 3 ± 7 and oxygen saturation nadir was 84 ± 8 %. Ten patients were initiated on non-invasive ventilation, one on supplemental oxygen and two had adenotonsillectomy. Conclusion Initial data shows high occurrence of SDB in patients pre and post ITB placement leading to medical or surgical intervention in 35%. Post ITB PSGs showed worsened oAHI and CAI and lower oxygen saturation nadir. Possible mechanisms include depression of central respiratory drive and decreased pulmonary reserves. This study may help stratify and address risks of ITB for those with refractory spasticity and SDB. Support None

0597 Precision of Sleep-Disordered Breathing Event Classification Using Simulated Home Sleep Apnea Testing in Patients with Spinal Cord Injury, or Disease

Abstract Introduction Sleep-disordered breathing (SDB) is highly prevalent among patients with spinal cord injury or disease (SCI/D). In-laboratory polysomnography (PSG) is difficult for these patients due to functional limitations and the physical construction of most sleep laboratories. Our objective was to evaluate the concordance between simulated HSAT and PSG in identifying SDB severity and subtypes of respiratory events in this patient population. Methods Within a larger study, 33 Veterans with SCI/D completed one night of in-laboratory PSG. Limited-channel HSAT was simulated by extracting 5 channels from PSG signals to include nasal pressure, thermistor, thoracic and abdominal belts, and oxygen saturation. Results Mean age of patients was 59.8 ± 10.9 years; 87.9% were male, and the average BMI was 28.1 ± 6.3. The mean Apnea-Hypopnea Index (AHI) from PSG was 35.5 ± 22.7. The mean Respiratory Event Index (REI) based on simulated HSAT was 22.5 ± 18.6. Thirty-one patients (93.9%) had SDB defined as AHI ≥5/hour. Simulated limited-channel HSAT accurately identified 32 out of 33 patients (96.96%). When SDB was further classified into mild (AHI 5-15 events/hr), moderate (AHI 15-30 events/hr), and severe (AHI&amp;gt;30/hr), simulated HSAT consistently underestimated the severity of underlying SDB. Spearman correlation between estimating AHI (PSG-HSAT) and subtypes of respiratory events was primarily accounted for by the difference in the number of hypopneas (r=0.72, -0.021 and -0.001 for hypopneas, obstructive and central apneas, respectively). Conclusion Our findings support the diagnostic utility of HSAT in SCI/D patients with SDB; however, HSAT underestimation of SDB may lead to difficulties in optimizing therapy. The misclassification of SDB severity is mainly driven by the number of hypopneas. Classification of hypopneas as obstrcutive or central may shed further light on the nature of this difference. Further research on the usability of HSAT devices in this patient population is needed. Support VA Rehabilitation Research and Development Service (RX002116; PI Badr and RX002885; PI Sankari) and NIH/NHLBI (K24HL143055; PI: Martin)

0633 Volume Assured Pressure Support is an Effective Treatment in Patients with Central Sleep Apnea Syndrome

Abstract Introduction Central sleep apnea syndrome (CSA) is commonly found in patients with congestive heart failure, brainstem disorders, and narcotic use. Various treatment modalities have been used with varied effectiveness in reducing the apnea-hypopnea index (AHI) and improving ventilation in patients with CSA. This study assessed whether Volume Assured Pressure Support (VAPS), a BiLevel mode of ventilation, is effective in treating CSA. Methods We performed a retrospective review of polysomnography (PSG) and VAPS titration studies on 11 patients at our institution: 7 patients had CSA with Cheyne-Stokes Respiration, 2 patients had CSA attributed to narcotic use, and 2 patients had primary CSA. CSA was diagnosed if more than 50% of the disordered breathing events were central. Five patients had failed a Continuous Positive Airway Pressure (CPAP) titration and then proceeded to VAPS while in 6 patients, VAPS was the initial treatment modality tried. We examined the effectiveness of VAPS in reducing AHI, improving oxygenation, and improving sleep architecture. Results Among the 11 patients, age was 63.0±12.1 yo, BMI was 33.7 ±4.5, 7 were males, Epworth sleepiness score was 9.3±4.9. The following significant changes from baseline PSG to VAPS titration were observed: AHI: 59.1± 8.0 to 27.2 ± 9.9 (p&amp;lt;.01); Time ≤ 88% O2 saturation (min): 48.1±14.5 to 15.4±6.1 (p&amp;lt;.05). Improvement in AHI was not related to gender, body mass index, narcotic use, or age. No significant changes in sleep architecture between the two studies were found. Ten (91%) patients had AHI &amp;gt; 30 on initial PSG. In 6 (55%) patients AHI was reduced to &amp;lt;15 with VAPS use. An additional patient had AHI reduced to 22.2, while 4 (36%) patients did not achieve an AHI &amp;lt; 30 with VAPS. Conclusion VAPS is an effective mode of treating CSA in the majority of patients. Support NA

Comparing manual and automatic scoring of sleep monitoring data from portable polygraphy.

We used sleep monitoring data from a study that investigated the prevalence, characteristics, risk factors and type of sleep apnea (SA) in 579 patients with paroxysmal atrial fibrillation. Most patients were screened for two nights, resulting in 1,043 sleep recordings that each contained data from one night. SA was diagnosed using the Nox T3 portable sleep monitor. An experienced sleep specialist scored the recordings manually using Noxturnal software. A total of 157 women (27%) and 422 men (73%) were examined; 477 (82.7%) had an apnea-hypopnea index (AHI) ≥ 5/hr, whereas moderate to severe SA (AHI≥15/hr) was diagnosed in 243 patients (42.1%). The AHI derived from automatic and manual scoring showed a good agreement (Pearson's r coefficient of 0.96). The median difference in AHI was very small (i.e., 0.72 [mean difference, 1.06]), but was statistically significant (p<.0001). Automatic scoring classified sleep recordings with more than 90% accuracy into SA categories of mild (AHI≥5/hr), moderate (AHI≥15/hr) and severe (AHI≥30/hr). We found a minor (11%-21%) mis-estimation of the number of recordings right above and below the boundary separating mild and moderate SA. The accuracy of automatic scoring differed from recording to recording, especially regarding the sensitivity of detecting disrupted breathing events. We found low to moderate agreement for the duration of disrupted breathing events (r=.53), for which the automatic scoring led to a statistically significant overestimation by 5.22s (p<.0001).

Continuous Focal Group Follows Operated by a Drone Enable Analysis of the Relation Between Sociality and Position in a Group of Male Risso’s Dolphins (Grampus griseus)

Relationships between social status and relative position of group-living animals have been described in a variety of species. For wild cetaceans, who spend most of their time underwater, collecting detailed, continuous data to assess such relationships depends highly on group size, formation, shyness of animals and observation platform. We test a new method for focal group sampling using an Unmanned Aerial Vehicle (UAV), focusing on one long-term followed group of 13 male Risso’s dolphins (Grampus griseus) in the Azores, Portugal. We aim to assess the usefulness of a UAV in delivering robust data to evaluate sociality in relation to relative position. Our analysis is based on recordings of synchronous breathing events, which are taken as an indicator of association strength. Twenty-one separate UAV flights were performed during seven surveys in July-August 2017, recording 2,886 breathing events and 571 synchronous dyads. Results showed strong differences in sociality between individuals and identified two strongly associated pairs, one strongly associated trio and six less associated individuals within the group. We subsequently created continuous time series of relative positions by interpolation of the positions recorded with the UAV at breathing events, and applied the Dynamic Time Warping method to assess associations based on relative position. This analysis identified more detailed association patterns than the synchrony analysis, and revealed a correlation between measures of sociality and relative position, at an individual and sub-cluster level, which may indicate dominant relationships. We compared results with those obtained with Photo-ID to assess any observation bias related to using a UAV. We found that 37% more breathing events were recorded with the UAV, and 21% more synchronous dyads detected, compared with Photo-ID, collected over the same observation periods, but, based on synchrony data, both methods yielded very similar results. We conclude that using a UAV for focal group follows of Risso’s dolphins enables a more granular study of association patterns than Photo-ID, by taking into account the relative position of individuals. The correlation found between measures of sociality and relative position holds promise for using UAVs in future studies of dominant relationships in Risso’s dolphins and other cetacean species.

Overnight polysomnography and the recording of sleep and sleep-related respiration in orchestra musicians - possible protective effects of wind instruments on respiration.

Our study is the first to objectively assess sleep and sleep-related respiration in orchestra musicians. We hypothesized low sleep quality due to high work demands and irregular work-sleep schedules, and a better respiration for wind instrument (WI) players than string instrument (SI) players due to habitual upper airway muscles training. We recorded overnight polysomnography with 29 professional orchestra musicians (21 men, 14 WI/ 15 SI). The musicians presented a sleep efficiency of 88% (IQR 82–92%) with WI having a significant higher sleep efficiency than SI (89%, 85–93% vs. 85%, 74–89%; p = 0.029). The group had a total sleep time around 6 hours (377min, 340-421min) with signs of increased NREM 1 (light sleep) and decreased REM (dream sleep). The musicians displayed an apnea-hypopnea-index of 2.1events/hour (0.7–5.5) and an oxygen saturation of 98% (97–100%). While SI player exhibited declining sleep-related respiration with age (breathing events: r = 0.774, p = 0.001, oxygen: r = -0.647, p = 0.009), WI player showed improved respiration with age (breathing events: r = -0.548, p = 0.043; oxygen: r = 0.610, p = 0.020). Our study is the first objective investigation of sleep pattern and respiration during sleep with overnight polysomnography in professional orchestra musicians. While sleep and respiration were unexpectedly good, our results revealed possible signs of sleep deprivation and an interesting age-related pattern on respiration depending on instrument. While sample size was small and results modest, these findings present first objective evidence towards the assumption that habitual playing of a WI–and training of the upper airway muscles–may have a protective effect on respiration.

Identification of Human Vital Functions Directly Relevant to the Respiratory System Based on the Cardiac and Acoustic Parameters and Random Forest

Regarding sleep research, polysomnography (PSG) also called a sleep study, is a gold standard. It incorporates brain waves, the oxygen level in the blood, heart rate and breathing, and leg movement recordings. PSG is a complicated and expensive laboratory-based procedure, usually done in hospitals or special sleep center. In this study, an alternative technique for Sleep-Related Breathing Disorders (SRBD) based on selected cardiac and acoustic parameters and the Random Forest (RF) has been studied. A system dedicated to the detection of simultaneously acquired ECG and acoustic signals, which are collected during sleep at home environment is proposed. Results obtained indicate that classification and regression tree models such as RF are appropriate for the evaluation of sleep disorders like SRBD. The best identification of sleep irregularities at level 89.00 percent for the raw database was obtained. Thus, statistical predictive models allow identification of breathing events with high levels of sensitivity and specificity, providing an inexpensive and accurate diagnosis.

Experimental study on typical characteristics of borehole breathing under different pressure and rock type conditions

Borehole breathing observed while drilling naturally fractured formations was often mistakenly interpreted as the influx of formation fluid or the loss of drilling fluids. Amongst several mechanisms that were caused for borehole ballooning, the opening and closing of finite fractures played a major role in naturally fractured formations. In this study, a novel experimental setup was designed to simulate the borehole breathing under different conditions. In this experiment, the typical characteristics of borehole breathing event under different pressure conditions and rock types were studied. Additionally, the influence of three important parameters on the cumulative loss of mud caused by borehole breathing was also investigated in detail. The results show that mud circulation pressure, fracture opening pressure and rock type had important influences on the maximum loss of mud and the speed at which the borehole breathing event occurs of borehole breathing, and were the key parameters for predicting the cumulative loss of mud. Based on analysis of the results, a new parameter, breathing coefficient αb which can be used to evaluate the borehole breathing event, was proposed, and its magnitude was proportional to the severity of the borehole breathing. This study provides theoretical support for the study of borehole breathing.

Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home

Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. OSA is also known to be position-dependent in some patients, which is referred to as positional OSA (pOSA). Screening for pOSA is necessary in order to design more personalized and effective treatment strategies. In this article, we propose analyzing accelerometry signals, recorded with a smartphone, to detect and monitor OSA at home. Our objectives were to: (1) develop an algorithm for detecting thoracic movement associated with disordered breathing events; (2) compare the performance of smartphones as OSA monitoring tools with a type 3 portable sleep monitor; and (3) explore the feasibility of using smartphone accelerometry to retrieve reliable patient sleep position data and assess pOSA. Accelerometry signals were collected through simultaneous overnight acquisition using both devices with 13 subjects. The smartphone tool showed a high degree of concordance compared to the portable device and succeeded in estimating the apnea-hypopnea index (AHI) and classifying the severity level in most subjects. To assess the agreement between the two systems, an event-by-event comparison was performed, which found a sensitivity of 90% and a positive predictive value of 80%. It was also possible to identify pOSA by determining the ratio of events occurring in a specific position versus the time spent in that position during the night. These novel results suggest that smartphones are promising mHealth tools for OSA and pOSA monitoring at home.

Interaction Between Slow Wave Sleep and Obstructive Sleep Apnea in Prevalent Hypertension.

Due to frequent abnormal breathing events and their effects on sleep architecture, patients with obstructive sleep apnea (OSA) exhibit decreased amounts of slow wave sleep (SWS). Reduced SWS has been linked to hypertension in community-based studies. We sought to investigate whether SWS percentage modifies the association between OSA and prevalent hypertension. We studied 7107 patients with OSA and 1118 primary snorers who underwent in-laboratory polysomnography. Patients were classified into quartiles of percent SWS. Hypertension was defined based either on clinic blood pressure measures or on physician diagnosis. Multivariable logistic regression model showed a significant interaction effect of OSA and SWS on prevalent hypertension (P=0.002). Decreased SWS was associated with higher odds for hypertension in OSA but not in primary snoring, with patients with OSA exhibiting <0.1% SWS (OR, 1.44 [95% CI, 1.21-1.70]; P=0.001) and those with 0.1% to 4.8% SWS (OR, 1.20 [95% CI, 1.03-1.40]; P=0.02) being more likely to have hypertension compared with those with >11.1% SWS. In analysis stratified by OSA severity, significant associations between percent SWS and blood pressure emerged only in moderate and severe OSA. Effect modifications by sex (P=0.040) and age (P=0.007) were also only evident in OSA, indicating that decreased SWS was associated with hypertension only in men and in patients <60 years old. Decreased SWS is associated with a dose-dependent increase in odds of prevalent hypertension in patients with OSA. The effects of SWS are likely to be modulated by OSA severity. SWS may be implicated in the heightened risk of cardiovascular diseases exhibited by patients with OSA.

EFFICACY OF A NOVEL DEVICE TO DETECT, ALERT AND RESOLVE NEONATAL APNEA - PILOT STUDY

Objective: To measure the accuracy of a novel device in detecting Bradycardia andDesaturation (B&amp;D) events and to determine its efficacy in resolving apneas innewborns with comparison to standard monitor (which only detects B&amp;D eventsand alerts).Design: This was a prospective observational study.Setting: Sick Newborn Care Unit of a large tertiary referral hospital in Hyderabad,India.Methods: 31 newborns were provided with a novel device, which monitored oxygensaturation and pulse rate and alarmed when values dropped below a set thresholdwhich is referred as an event, henceforth. The novel device also provided footstimulation in response to above-mentioned events. When the monitor alarmed, anurse attended to the baby to confirm whether the baby was breathing and whetherthe event had been resolved by the device. If the event had not resolved, appropriateaction as per the standard-of-care was performed.Results: The novel device “ApneBootTM” positively detected B&amp;D events 94.03% oftimes as compared to the standard reference monitor. 56 of 67 observed B&amp;D eventswere visually confirmed to be apneas, indicating that 83.6% of B&amp;D eventscoincided with apneas. Of the 56 apneic events, 50 were central apneas, of which 35were resolved by the novel device, making the device’s efficacy of apnea resolution70%.Conclusion: The results of the study indicate that this novel device “ApneBootTM”is very effective in detecting and alarming B&amp;D events, which coincides with theapnea, and resolving it by providing foot stimulation.Keywords: Novel Device, Neonatal Apnea, Low Birth Weight, Body Temperature,Kangaroo Mother Care, Community Health,

Breathing prediction of fracture deviated wellbore through flooding mechanism

Wellbore breathing is a common occurrence during drilling operations, but the down hole mechanism and how it manifests itself in surface and subsurface processes, is not well understood. It often result in a drilling fluid gain at surface and are misidentified as a kick, resulting in unneeded shut in periods and associated non-productive time (NPT). Further, misidentification of wellbore breathing as underbalance often results in increases in mud weight, which only exacerbates the problem and may cause lost circulation. This work focuses on characterization of the wellbore breathing phenomenon in practical contexts of the Pressure, volume, temperature and time behavior of the components involved in the surface and subsurface system. Methods for identification, differentiation from kicks and underbalance, mitigation and prevention of wellbore breathing are proposed, in addition to operational procedures for safe continuance of drilling operations. The coupled nature of Pressure, volume, temperature as they connect to wellbore breathing, are analyzed in detail in order to quantify their effects and how they can inhibit identification of wellbore breathing. There are many complications associated with abnormally high fluid pressures in over pressured formations. Pore pressure will directly influence all components of operations as well as drilling, earth science studies, completion, and production. Correct predictions of pore pressure and fracture pressure area unit very important aspects to the assembly and completion of safe, time economical, and price economical comes. Information of pressure distribution within the formation will greatly cut back complexities related to drilling and finishing a well. This research also proposes adaptation of the hydraulic fracturing pump-in flow back test for the interpretation of wellbore breathing events and estimation of the minimum horizontal stress. This work presents estimates of the minimum horizontal stress for fractured zones in five wells using PWD data recorded during wellbore breathing events. These estimates were verified qualitatively, when possible, by comparing across multiple connections and with available LOT data.