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Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

ABSTRACT: BACKGROUND:Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). OBJECTIVE:The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. METHODS:Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. RESULTS:Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an F1 score of 89% in differentiating OA vs CA. CONCLUSIONS:In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition.

SUBMITTER: Akbarian S

PROVIDER: S-EPMC7275259 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

Akbarian Sina S Montazeri Ghahjaverestan Nasim N Yadollahi Azadeh A Taati Babak B

Journal of medical Internet research 20200522 5

<h4>Background</h4>Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controve ...[more]

PMID: 32441656

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Project description:Background: Although central apneas (CA) and obstructive apneas (OA) are highly prevalent in heart failure (HF), a comparison of apnea prevalence, predictors and clinical correlates in the whole HF spectrum, including HF with reduced ejection fraction (HFrEF), mid-range EF (HFmrEF) and preserved EF (HFpEF) has never been carried out so far. Materials and methods: 700 HF patients were prospectively enrolled and then divided according to left ventricular EF (408 HFrEF, 117 HFmrEF, 175 HFpEF). All patients underwent a thorough evaluation including: 2D echocardiography; 24-h Holter-ECG monitoring; cardiopulmonary exercise testing; neuro-hormonal assessment and 24-h cardiorespiratory monitoring. Results: In the whole population, prevalence of normal breathing (NB), CA and OA at daytime was 40, 51, and 9%, respectively, while at nighttime 15, 55, and 30%, respectively. When stratified according to left ventricular EF, CA prevalence decreased (daytime: 57 vs. 43 vs. 42%, p = 0.001; nighttime: 66 vs. 48 vs. 34%, p < 0.0001) from HFrEF to HFmrEF and HFpEF, while OA prevalence increased (daytime: 5 vs. 8 vs. 18%, p < 0.0001; nighttime 20 vs. 29 vs. 53%, p < 0.0001). In HFrEF, male gender and body mass index (BMI) were independent predictors of both CA and OA at nighttime, while age, New York Heart Association functional class and diastolic dysfunction of daytime CA. In HFmrEF and HFpEF male gender and systolic pulmonary artery pressure were independent predictors of CA at daytime, while hypertension predicted nighttime OA in HFpEF patients; no predictor of nighttime CA was identified. When compared to patients with NB, those with CA had higher neuro-hormonal activation in all HF subgroups. Moreover, in the HFrEF subgroup, patients with CA were older, more comorbid and with greater hemodynamic impairment while, in the HFmrEF and HFpEF subgroups, they had higher left atrial volumes and more severe diastolic dysfunction, respectively. When compared to patients with NB, those with OA were older and more comorbid independently from background EF. Conclusions: Across the whole spectrum of HF, CA prevalence increases and OA decreases as left ventricular systolic dysfunction progresses. Different predictors and specific clinical characteristics might help to identify patients at risk of developing CA or OA in different HF phenotypes.

Project description:Study objectivesWe investigated the diagnostic accuracy for the identification of obstructive sleep apnea (OSA) and its severity of a noninvasive technology based on image processing (SleepWise).MethodsThis is an observational, prospective study to evaluate the degree of agreement between polysomnography (PSG) and SleepWise. We recruited 56 consecutive subjects with suspected OSA who were referred as outpatients to the Sleep Unit of the Hospital Universitari Germans Trias i Pujol (HUGTiP) from January 2013 to January 2014. All patients underwent laboratory PSG and image processing with SleepWise simultaneously the same night. Both PSG and SleepWise analyses were carried independently and blindly.ResultsWe analyzed 50 of the 56 patients recruited. OSA was diagnosed through PSG in a total of 44 patients (88%) with a median apnea-hypopnea index (AHI) of 25.35 (24.9). According to SleepWise, 45 patients (90%) met the criteria for a diagnosis of OSA, with a median AHI of 22.8 (22.03). An analysis of the ability of PSG and SleepWise to classify patients by severity on the basis of their AHI shows that the two diagnostic systems distribute the different groups similarly. According to PSG, 23 patients (46%) had a diagnosis of severe OSA, 11 patients (22%) moderate OSA, and 10 patients (20%) mild OSA. According to SleepWise, 20, 13, and 12 patients (40%, 26%, and 24%, respectively) had a diagnosis of severe, moderate, and mild OSA respectively. For OSA diagnosis, SleepWise was found to have sensitivity of 100% and specificity of 83% in relation to PSG. The positive predictive value was 97% and the negative predictive value was 100%. The Bland-Altman plot comparing the mean AHI values obtained through PSG and SleepWise shows very good agreement between the two diagnostic techniques, with a bias of -3.85, a standard error of 12.18, and a confidence interval of -0.39 to -7.31.ConclusionsSleepWise was reasonably accurate for noninvasive and automatic diagnosis of OSA in outpatients. SleepWise determined the severity of OSA with high reliability. The current study including simultaneous laboratory PSG and SleepWise processing image is proposed as a reasonable validation standard.

Project description:BackgroundSleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list.ObjectiveThe objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea.MethodsA noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model.ResultsThe algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%.ConclusionsThis study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

Project description:BackgroundObstructive sleep apnea is associated with hypertension, inflammation, and increased cardiovascular risk. Continuous positive airway pressure (CPAP) reduces blood pressure, but adherence is often suboptimal, and the benefit beyond management of conventional risk factors is uncertain. Since intermittent hypoxemia may underlie cardiovascular sequelae of sleep apnea, we evaluated the effects of nocturnal supplemental oxygen and CPAP on markers of cardiovascular risk.MethodsWe conducted a randomized, controlled trial in which patients with cardiovascular disease or multiple cardiovascular risk factors were recruited from cardiology practices. Patients were screened for obstructive sleep apnea with the use of the Berlin questionnaire, and home sleep testing was used to establish the diagnosis. Participants with an apnea-hypopnea index of 15 to 50 events per hour were randomly assigned to receive education on sleep hygiene and healthy lifestyle alone (the control group) or, in addition to education, either CPAP or nocturnal supplemental oxygen. Cardiovascular risk was assessed at baseline and after 12 weeks of the study treatment. The primary outcome was 24-hour mean arterial pressure.ResultsOf 318 patients who underwent randomization, 281 (88%) could be evaluated for ambulatory blood pressure at both baseline and follow-up. On average, the 24-hour mean arterial pressure at 12 weeks was lower in the group receiving CPAP than in the control group (-2.4 mm Hg; 95% confidence interval [CI], -4.7 to -0.1; P=0.04) or the group receiving supplemental oxygen (-2.8 mm Hg; 95% CI, -5.1 to -0.5; P=0.02). There was no significant difference in the 24-hour mean arterial pressure between the control group and the group receiving oxygen. A sensitivity analysis performed with the use of multiple imputation approaches to assess the effect of missing data did not change the results of the primary analysis.ConclusionsIn patients with cardiovascular disease or multiple cardiovascular risk factors, the treatment of obstructive sleep apnea with CPAP, but not nocturnal supplemental oxygen, resulted in a significant reduction in blood pressure. (Funded by the National Heart, Lung, and Blood Institute and others; HeartBEAT ClinicalTrials.gov number, NCT01086800 .).

Dataset Information

Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

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Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

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