Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

Kim, Jeong Whun; Kim, Tae-Hoon; Shin, Jaeyoung; Choe, Goun; Lim, Hyun Jung; Rhee, Chae Seo; Lee, Kyogu; Cho, Sung Woo

doi:10.21053/ceo.2018.00388

Cited by 20 publications

(28 citation statements)

References 23 publications

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“…This is also consistent with our previous findings, which evaluated a binary classification of OSA. 6 Accuracy incorporates both bias and precision, and among evaluation metrics for accuracy, we used P 50 (ie, deviation >50% from mAHI), which is somewhat arbitrary and, therefore, needs further validation. This value is also a relative measure in which accuracy varies according to the level of AHI and does not have consistent meaning across the entire range.…”

Section: Discussionmentioning

confidence: 99%

“…Accuracies of binary prediction were about 82.7% to 85.3%. 6 Binary prediction is important for prescreening since based on the prediction result, patients or clinicians may actually take action on whether to go through further study such as full-night polysomnography. However, prediction of AHI would be more intuitive and informative from a treatment perspective since it tells the amount of improvement.…”

Section: Discussionmentioning

confidence: 99%

“…Sound processing was conducted as previously described. 6 Briefly, noise reduction preprocessing was followed by data segmentation into 5-second windows and sound features were then extracted from each windowed signal. These procedures were performed using jAudio, a Java-based audio feature extraction software.…”

Section: Methodsmentioning

confidence: 99%

“…Our previous study demonstrated the possibility of prescreening for OSA using a binary classifier from respiratory sounds during sleep. [5][6][7] In particular, we have simplified our study approach by using whole sound data during sleep and unbiased feature selection. 6 In this study, we attempted to predict the apnea-hypopnea index (AHI) by analyzing breathing sounds generated during sleep.…”

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confidence: 99%

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Prediction of Apnea‐Hypopnea Index Using Sound Data Collected by a Noncontact Device

Kim¹,

Kim

Shin

et al. 2020

Otolaryngol.--head neck surg.

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Objective To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep. Study Design Prospective cohort study. Setting Tertiary referral hospital. Subject and Methods Audio recordings during sleep were performed using an air-conduction microphone during polysomnography. Breathing sounds recorded from all sleep stages were analyzed. After noise reduction preprocessing, the audio data were segmented into 5-second windows and sound features were extracted. Estimation of AHI by regression analysis was performed using a Gaussian process, support vector machine, random forest, and simple linear regression, along with 10-fold cross-validation. Results In total, 116 patients who underwent attended, in-laboratory, full-night polysomnography were included. Overall, random forest resulted in the highest performance with the highest correlation coefficient (0.83) and least mean absolute error (9.64 events/h) and root mean squared error (13.72 events/h). Other models resulted in somewhat lower but similar performances, with correlation coefficients ranging from 0.74 to 0.79. The estimated AHI tended to be underestimated as the severity of OSA increased. Regarding bias and precision, estimation performances in the severe OSA subgroup were the lowest, regardless of the model used. Among sound features, derivative of the area methods of moments of overall standard deviation demonstrated the highest correlation with AHI. Conclusion AHI was fairly predictable by using data from breathing sounds generated during sleep. The prediction model may be useful not only for prescreening but also for follow-up after treatment in patients with OSA.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Prediction of Apnea‐Hypopnea Index Using Sound Data Collected by a Noncontact Device

Kim¹,

Kim

Shin

et al. 2020

Otolaryngol.--head neck surg.

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“…In turn, this issue is commonly associated with the awakening of the subject across nighttime leading to sleep fragmentation. On the next day, and as a consequence, sleep fragmentation produces EDS [41][42][43], which is very common in older adults [44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

The Endocannabinoid System May Modulate Sleep Disorders in Aging

Murillo-Rodrı́guez

Budde

Veras

et al. 2020

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Aging is an inevitable process that involves changes across life in multiple neurochemical, neuroanatomical, hormonal systems, and many others. In addition, these biological modifications lead to an increase in age-related sickness such as cardiovascular diseases, osteoporosis, neurodegenerative disorders, and sleep disturbances, among others that affect activities of daily life. Demographic projections have demonstrated that aging will increase its worldwide rate in the coming years. The research on chronic diseases of the elderly is important to gain insights into this growing global burden. Novel therapeutic approaches aimed for treatment of age-related pathologies have included the endocannabinoid system as an effective tool since this biological system shows beneficial effects in preclinical models. However, and despite these advances, little has been addressed in the arena of the endocannabinoid system as an option for treating sleep disorders in aging since experimental evidence suggests that some elements of the endocannabinoid system modulate the sleep-wake cycle. This article addresses this less-studied field, focusing on the likely perspective of the implication of the endocannabinoid system in the regulation of sleep problems reported in the aged. We conclude that beneficial effects regarding the putative efficacy of the endocannabinoid system as therapeutic tools in aging is either inconclusive or still missing.

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Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds

Cho

Jung

Shin

et al. 2022

JAMA Otolaryngol Head Neck Surg

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Breathing 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.OBJECTIVE To 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 MEASURES Accuracy of OSA prediction models.RESULTS A 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.

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Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

Cited by 20 publications

References 23 publications

Prediction of Apnea‐Hypopnea Index Using Sound Data Collected by a Noncontact Device

Prediction of Apnea‐Hypopnea Index Using Sound Data Collected by a Noncontact Device

The Endocannabinoid System May Modulate Sleep Disorders in Aging

Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds

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