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%

Dynamics of Snoring Sounds and Its Connection with Obstructive Sleep Apnea

Snoring is extremely common in the general population and when irregular may indicate the presence of obstructive sleep apnea. We analyze the overnight sequence of wave packets --- the snore sound --- recorded during full polysomnography in patients referred to the sleep laboratory due to suspected obstructive sleep apnea. We hypothesize that irregular snore, with duration in the range between 10 and 100 seconds, correlates with respiratory obstructive events. We find that the number of irregular snores --- easily accessible, and quantified by what we call the snore time interval index (STII) --- is in good agreement with the well-known apnea-hypopnea index, which expresses the severity of obstructive sleep apnea and is extracted only from polysomnography. In addition, the Hurst analysis of the snore sound itself, which calculates the fluctuations in the signal as a function of time interval, is used to build a classifier that is able to distinguish between patients with no or mild apnea and patients with moderate or severe apnea

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

Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed

Obstructive sleep apnea severity affects amyloid burden in cognitively normal elderly a longitudinal study

Recent evidence suggests that Obstructive Sleep Apnea (OSA) may be a risk factor for developing Mild Cognitive Impairment and Alzheimer’s disease. However, how sleep apnea affects longitudinal risk for Alzheimer’s disease is less well understood.Postprint (author's final draft

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