Treatment of cardiomyopathy with PAP therapy in a patient with severe obstructive sleep apnea.

Obstructive sleep apnea is common in patients with heart failure. This case illustrates that treatment with PAP therapy can improve cardiac function in patients with both conditions. CPAP-emergent central apnea, as seen in this patient, has multiple etiologies. It is commonly seen in patients with severe sleep apnea, usually resolves over time, and does not need treatment with adaptive servoventilation

Apnea Monitor Based on Bluetooth with Android Interface

Apnea monitor is a device that is used to give a warning if there is stop breathing. Stop breathing while sleeping is one form of obstructive sleep apnea. This cessation of breath cannot be underestimated, this is related to the main risk factors for health implications and increased cardiovascular disease and sudden death. The purpose of this study is to design an apnea monitor with the Android interface. This device allows the users to get how many times sleep apnea happens while sleeping and got data to analysis before continuing with a more expensive and advanced sleep test. This device used a flex sensor to detect the respiration rate, the sensor placed on the abdomen or belly so it can measure expand and deflate while breathing. The microcontroller uses an Arduino chip called AT-Mega328. Bluetooth HC-05 used to send respiration data to Android, MIT app inventor used for the android programmer, and on the android, there are plotting of respiration value and when the device detected apnea so the android also gives a warning to the user. Based on the results of testing and measurement then compare with another device, the results of the average% error were 3.61%. This apnea monitor design is portable but there are needs some improvement by using another sensor for detected respiration and using a module other than Bluetooth

Non-linear HRV analysis to quantify the effects of intermittent hypoxia using an OSA rat model

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, a non-linear HRV analysis was performed to assess fragmentation signatures observed in heartbeat time series after intermittent hypoxia (IH). Three markers quantifying short-term fragmentation levels, PIP, IALS and PSS, were evaluated on R-R interval series obtained in a rat model of recurrent apnea. Through airway obstructions, apnea episodes were periodically simulated in six anesthetized Sprague-Dawley rats. The number of apnea events per hour (AHI index) was varied during the first half of the experiment while apnea episodes lasted 15 s. For the second part, apnea episodes lasted 5, 10 or 15 s, but the AHI index was fixed. Recurrent apnea was repeated for 15-min time intervals in all cases, alternating with basal periods of the same duration. The fragmentation markers were evaluated in segments of 5 minutes, selected at the beginning and end of the experiment. The impact of the heart and breathing rates (HR and BR, respectively) on the parameter estimates was also investigated. The results obtained show a significant increase (from 5 to 10%, p 0.9) between these markers and BR, as well as with the ratio given by HR/BR. Although fragmentation may be impacted by IH, we found that it is highly dependent on HR and BR values and thus, they should be considered during its calculation or used to normalize the fragmentation estimatesPeer ReviewedPostprint (published version

Obstructive sleep apnea syndrome and perioperative complications: a systematic review of the literature.

Obstructive sleep apnea syndrome (OSAS) is a common sleep related breathing disorder. Its prevalence is estimated to be between 2% and 25% in the general population. However, the prevalence of sleep apnea is much higher in patients undergoing elective surgery. Sedation and anesthesia have been shown to increase the upper airway collapsibility and therefore increasing the risk of having postoperative complications in these patients. Furthermore, the majority of patients with sleep apnea are undiagnosed and therefore are at risk during the perioperative period. It is important to identify these patients so that appropriate actions can be taken in a timely fashion. In this review article, we will discuss the epidemiology of sleep apnea in the surgical population. We will also discuss why these patients are at a higher risk of having postoperative complications, with the special emphasis on the role of anesthesia, opioids, sedation, and the phenomenon of REM sleep rebound. We will also review how to identify these patients preoperatively and the steps that can be taken for their perioperative management

The Bidirectional Relationship Between Obstructive Sleep Apnea and Metabolic Disease

Obstructive sleep apnea (OSA) is a common sleep disorder, effecting 17% of the total population and 40–70% of the obese population (1, 2). Multiple studies have identified OSA as a critical risk factor for the development of obesity, diabetes, and cardiovascular diseases (3–5). Moreover, emerging evidence indicates that metabolic disorders can exacerbate OSA, creating a bidirectional relationship between OSA and metabolic physiology. In this review, we explore the relationship between glycemic control, insulin, and leptin as both contributing factors and products of OSA. We conclude that while insulin and leptin action may contribute to the development of OSA, further research is required to determine the mechanistic actions and relative contributions independent of body weight. In addition to increasing our understanding of the etiology, further research into the physiological mechanisms underlying OSA can lead to the development of improved treatment options for individuals with OSA

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Long-term prediction of adherence to continuous positive air pressure therapy for the treatment of moderate/severe obstructive sleep apnea syndrome

BACKGROUND: Continuous positive airway pressure (CPAP) therapy is a highly effective treatment for obstructive sleep apnea syndrome (OSAS). However, poor adherence is a limiting factor, and a significant proportion of patients are unable to tolerate CPAP. The aim of this study was to determine predictors of long-term non-compliance with CPAP. METHODS: CPAP treatment was prescribed to all consecutive patients with moderate or severe OSAS (AHI ≥15 events/h) (n = 295) who underwent a full-night CPAP titration study at home between February 1, 2002 and December 1, 2016. Adherence was defined as CPAP use for at least 4 h per night and five days per week. Subjects had periodical follow-up visits including clinical and biochemical evaluation and assessment of adherence to CPAP. RESULTS: Median follow-up observation was 74.8 (24.2/110.9) months. The percentage of OSAS patients adhering to CPAP was 41.4% (42.3% in males and 37.0% in females), and prevalence was significantly higher in severe OSAS than in moderate (51.8% vs. 22.1%; p < 0.001; respectively). At multivariate analysis, lower severity of OSAS (HR = 0.66; CI 95 0.46-0.94) p < 0.023), cigarette smoking (HR = 1.72; CI 95 1.13-2.61); p = 0.011), and previous cardiovascular events (HR = 1.95; CI 95 1.03-3.70; p = 0.04) were the only independent predictors of long-term non-adherence to CPAP after controlling for age, gender, and metabolic syndrome. CONCLUSIONS: In our cohort of patients with moderate/severe OSAS who were prescribed CPAP therapy, long-term compliance to treatment was present in less than half of the patients. Adherence was positively associated with OSAS severity and negatively associated with cigarette smoking and previous cardiovascular events at baseline

Association between obstructive apnea syndrome during sleep and damages to anterior labyrinth: Our experience

The obstructive sleep apnea syndrome is a chronic condition characterized by frequent episodes of collapse of the upper airways during sleep. It can be considered a multisystem disease. Among the districts involved, even the auditory system was seen to be concerned. It was enrolled a population of 20 patients after polysomnographic diagnosis of OSAS (Apnea Hypopnea Index > 10) and a control group of 28 healthy persons (Apnea Hypopnea Index < 5). Each patient has been subjected to Pure Tone Audiometry, Tympanometry, study of Acoustic Reflex, Otoacoustic Emissions and Auditory Brainstem Response. Moreover they were submitted to endoscopy of upper airway with Muller Maneuver and Epworth Sleepiness Scale (ESS). The values of ESS was 13.5 in OSAS group and 5.4 in control group. The tone audiometry is worse in all frequencies analyzed in OSAS patients, but within the normal range for both groups analyzed by 250 to 1000 Hertz. Otoacoustic emissions show a reduced reproducibility and a lower signal/ noise ratio in OSAS group (P <0.01)

Sleep apnea-hypopnea quantification by cardiovascular data analysis

Sleep apnea is the most common sleep disturbance and it is an important risk factor for cardiovascular disorders. Its detection relies on a polysomnography, a combination of diverse exams. In order to detect changes due to sleep disturbances such as sleep apnea occurrences, without the need of combined recordings, we mainly analyze systolic blood pressure signals (maximal blood pressure value of each beat to beat interval). Nonstationarities in the data are uncovered by a segmentation procedure, which provides local quantities that are correlated to apnea-hypopnea events. Those quantities are the average length and average variance of stationary patches. By comparing them to an apnea score previously obtained by polysomnographic exams, we propose an apnea quantifier based on blood pressure signal. This furnishes an alternative procedure for the detection of apnea based on a single time series, with an accuracy of 82%

Evaluation of human obstructive sleep apnea using computational fluid dynamics.

Obstructive sleep apnea (OSA) severity might be correlated to the flow characteristics of the upper airways. We aimed to investigate the severity of OSA based on 3D models constructed from CT scans coupled with computational fluid dynamics (CFD) simulations. The CT scans of seven adult patients diagnosed with OSA were used to reconstruct the 3D models of the upper airways and CFD modeling and analyses were performed. Results from the fluid simulations were compared with the apnea-hypopnea index. Here we show a correlation between a CFD-based parameter, the adjusted pressure coefficient (Cp*), and the respective apnea-hypopnea index (Pearson's r = 0.91, p = 0.004), which suggests that the anatomical-based model coupled with CFD could provide functional and localized information for different regions of the upper airways