scholarly journals Yolo4Apnea: Real-time Detection of Obstructive Sleep Apnea

2020 ◽  
Author(s):  
Sondre Hamnvik ◽  
Pierre Bernabé ◽  
Sagar Sen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Heart Disease ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Real Time ◽  
Source Code ◽  
Learning System ◽  
Breathing Patterns ◽  
Obstructive Sleep ◽  
Abdominal Breathing

Obstructive sleep apnea is a serious sleep disorder that affects an estimated one billion adults worldwide. It causes breathing to repeatedly stop and start during sleep which over years increases the risk of hypertension, heart disease, stroke, Alzheimer's, and cancer. In this demo, we present Yolo4Apnea a deep learning system extending You Only Look Once (Yolo) system to detect sleep apnea events from abdominal breathing patterns in real-time enabling immediate awareness and action. Abdominal breathing is measured using a respiratory inductance plethysmography sensor worn around the stomach. The source code is available at https://github.com/simula-vias/Yolo4Apnea

scholarly journals Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6481
Author(s):  
Kristin McClure ◽  
Brett Erdreich ◽  
Jason H. T. Bates ◽  
Ryan S. McGinnis ◽  
Axel Masquelin ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Wearable Sensors ◽  
Rapid Assessment ◽  
Central Sleep Apnea ◽  
Breathing Patterns ◽  
Movement Data ◽  
Normal Breathing ◽  
Obstructive Sleep

Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.

scholarly journals Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Korkalainen ◽  
Timo Leppanen ◽  
Juhani Aakko ◽  
Sami Nikkonen ◽  
Samu Kainulainen ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Clinical Population ◽  
Sleep Staging ◽  
Obstructive Sleep

Is cormobid insomnia in obstructive sleep apnea linked to heart disease?

2017 ◽  
Vol 40 ◽  
pp. e62
Author(s):  
Y.W. Cho ◽  
K.T. Kim ◽  
H.-J. Moon ◽  
K.I. Yang
Keyword(s):  
Obstructive Sleep Apnea ◽  
Heart Disease ◽  
Sleep Apnea ◽  
Obstructive Sleep

Abstract 14771: Obstructive Sleep Apnea is Associated With Cardiac Dysfunction in Children With Congenital Heart Disease

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Daniel Combs ◽  
Vanessa Fernandez ◽  
brent j barber ◽  
Wayne J Morgan ◽  
Chiu-Hsieh Hsu ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Obstructive Sleep Apnea ◽  
Heart Disease ◽  
Sleep Apnea ◽  
Right Ventricular ◽  
Cardiac Dysfunction ◽  
Congenital Heart ◽  
Left Ventricular ◽  
Sleep Study ◽  
Obstructive Sleep

Introduction: Obstructive sleep apnea (OSA) is associated with cardiac dysfunction in children without congenital heart disease (CHD). Children with CHD are at increased risk for OSA and may be susceptible to further cardiovascular consequences due to OSA but the extent and nature of such cardiovascular effects of OSA are unknown. Methods: Children (6-17 years old) with corrected CHD without current cyanosis or Down syndrome were recruited from pediatric cardiology clinic. Home sleep tests were done to determine the presence and severity of OSA. OSA was defined as an obstructive apnea hypopnea index (oAHI) ≥1. Mild OSA was defined as an oAHI of ≥1 to <5 and moderate OSA was defined as an oAHI of ≥5 to <10. Standard clinically indicated echocardiograms were performed in clinic. Echocardiographic findings were compared between children with CHD with and without comorbid OSA using t-tests, Wilcoxon-sign rank tests as well as linear or logistic regression as appropriate. Results: Thirty-two children had sleep study and echocardiographic data available. OSA was present in 18 children (56%). OSA was mild in 89% and moderate in 11% of cases. There were no significant differences in age, body mass index, CHD severity, gender or ethnicity between children with and without OSA. Children with OSA had larger height-indexed right ventricular end-diastolic diameter (RVDi) compared to those without OSA (median 1.35, 95% CI 1.09, 1.56 vs. 1.21, 95% CI 1.01, 1.57; p=0.04). Children with moderate OSA had a reduced left ventricular shortening fraction compared to both those with mild OSA and no OSA (30.0 ± 6.1% vs. 38.7 ± 4.4%; p=0.009 and 39.2 ± 3.6%; p=0.007, respectively). Children with moderate OSA had increased left ventricular end-systolic diameter compared to those with mild OSA and no OSA (3.4 ± 0.4 cm vs. 2.5 ± 0.4; p=0.007 and 2.4 ± 0.5; p=0.001, respectively). Children with an RVDi above the median were seven times more likely to have OSA than those with an RVDi below the median (odds ratio 6.9.; 95% CI 1.3, 35; p=0.02). Conclusions: OSA is associated with changes in cardiac morphology and reduced contractility in children with CHD. Additionally, the presence of right ventricular dilation may suggest the need for OSA evaluation in children with CHD.

scholarly journals 0439 Nonlinear Dynamics Forecasting for Personalize Prognosis of Obstructive Sleep Apnea Onsets

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A168-A169
Author(s):  
T Le
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Real Time ◽  
Dirichlet Process ◽  
Clinical Symptoms ◽  
Risk Indicators ◽  
Dirichlet Process Mixture ◽  
Obstructive Sleep ◽  
Estimated Risk ◽  
Normal States

Abstract Introduction The emphasis on disease prevention, early detection, and preventive treatments will revolutionize the way sleep clinicians evaluate their patients. Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with approximately 100 millions patients been diagnosed worldwide. The effectiveness of sleep disorder therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. Methods This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. The method includes (a) a representation of a transition state space network to characterize dynamic transition of apneic states (b) a Dirichlet-Process Mixture-Gaussian-Process prognostic method for estimating the distribution of the time estimate the remaining time until the onset of an impending OSA episode by considering the stochastic evolution of the normal states to an anomalous (apnea) Results The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of eight subjects from previous work. The average prediction accuracy (R2) is reported as 0.75%, with 87% of observations within the 95% confidence interval. Estimated risk indicators at 1 to 3 min till apnea onset are reported as 85.8 %, 80.2 %, and 75.5 %, respectively. Conclusion The present prognosis approach can be integrated with wearable devices to facilitate individualized treatments and timely prevention therapies. Support N/A

scholarly journals HEART RATE VARIABILITY IN PATIENTS WITH OBSTRUCTIVE SLEEP APNEA SYNDROME, PRIMARY SNORING AND ISCHEMIC HEART DISEASE

2018 ◽  
Vol 15 (3) ◽  
pp. 286-292
Author(s):  
A. Bulgak ◽  
E. Tarasik
Keyword(s):  
Obstructive Sleep Apnea ◽  
Heart Disease ◽  
Sleep Apnea ◽  
Ischemic Heart Disease ◽  
Sinus Node ◽  
Sleep Apnea Syndrome ◽  
Ischemic Heart ◽  
Primary Snoring ◽  
Obstructive Sleep ◽  
The Impact

The purpose of our study is to assess the impact of cardiac arrhythmias, heart rhythm variability in patients with ischemic heart disease, obstructive sleep apnea and primary snoring. 65 patients at an age of 40–68 years with ischemic heart disease, obstructive sleep apnea and primary snoring were researched.Obstructive sleep apnea and primary snoring lead to an increase in the sympathetic and parasympathetic activity of the autonomic nervous system on the sinus node in patients with ischemic heart disease, obstructive sleep apnea and primary snoring.

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