Dataset Information

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

ABSTRACT:

Background

Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home.

Objective

The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist.

Methods

This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI).

Results

Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F₁-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively.

Conclusions

Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

SUBMITTER: Le VL

PROVIDER: S-EPMC9996414 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

Le Vu Linh VL Kim Daewoo D Cho Eunsung E Jang Hyeryung H Reyes Roben Delos RD Kim Hyunggug H Lee Dongheon D Yoon In-Young IY Hong Joonki J Kim Jeong-Whun JW

Journal of medical Internet research 20230222

<h4>Background</h4>Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home.<h4>Objective</h4>The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home envi ...[more]

PMID: 36811943

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Project description:ImportanceBreathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes.ObjectiveTo evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection.Design, setting, and participantsA cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020.Main outcomes and measuresAccuracy of OSA prediction models.ResultsA total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = -0.20; 95% CI, -0.35 to -0.05) were found to be associated with estimation error.Conclusions and relevanceIn this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.

Project description:BackgroundThe American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.ObjectiveThis study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.MethodsA total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).ResultsA total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).ConclusionsDefined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Project description:Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis. Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture. Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference. Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.

Project description:Study objectivesObstructive sleep apnea is associated with neurobehavioral dysfunction, but the relationship between disease severity as measured by the apnea-hypopnea index and neurobehavioral morbidity is unclear. The objective of our study is to compare the neurobehavioral morbidity of mild sleep-disordered breathing versus obstructive sleep apnea.MethodsChildren 3-12 years old recruited for mild sleep-disordered breathing (snoring with obstructive apnea-hypopnea index < 3) into the Pediatric Adenotonsillectomy Trial for Snoring were compared to children 5-9 years old recruited for obstructive sleep apnea (obstructive apnea-hypopnea 2-30) into the Childhood Adenotonsillectomy Trial. Baseline demographic, polysomnographic, and neurobehavioral outcomes were compared using univariable and multivariable analysis.ResultsThe sample included 453 participants with obstructive sleep apnea (median obstructive apnea-hypopnea index 5.7) and 459 participants with mild sleep-disordered breathing (median obstructive apnea-hypopnea index 0.5). By polysomnography, participants with obstructive sleep apnea had poorer sleep efficiency and more arousals. Children with mild sleep-disordered breathing had more abnormal executive function scores (adjusted odds ratio 1.96, 95% CI 1.30-2.94) compared to children with obstructive sleep apnea. There were also elevated Conners scores for inattention (adjusted odds ratio 3.16, CI 1.98-5.02) and hyperactivity (adjusted odds ratio 2.82, CI 1.83-4.34) in children recruited for mild sleep-disordered breathing.ConclusionsAbnormal executive function, inattention, and hyperactivity were more common in symptomatic children recruited into a trial for mild sleep-disordered breathing compared to children recruited into a trial for obstructive sleep apnea. Young, snoring children with only minimally elevated apnea-hypopnea levels may still be at risk for deficits in executive function and attention.Trial registrationPediatric Adenotonsillectomy for Snoring (PATS), NCT02562040; Childhood Adenotonsillectomy Trial (CHAT), NCT00560859.

Dataset Information

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

Background

Objective

Methods

Results

Conclusions

Publications

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

Similar Datasets

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