Dataset Information

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography.

ABSTRACT:

Purpose

Diagnosis of obstructive sleep apnea by the gold-standard of polysomnography (PSG), or by home sleep testing (HST), requires numerous physical connections to the patient which may restrict use of these tools for early screening. We hypothesized that normal and disturbed breathing may be detected by a consumer smartphone without physical connections to the patient using novel algorithms to analyze ambient sound.

Methods

We studied 91 patients undergoing clinically indicated PSG. Phase I: In a derivation cohort (n = 32), we placed an unmodified Samsung Galaxy S5 without external microphone near the bed to record ambient sounds. We analyzed 12,352 discrete breath/non-breath sounds (386/patient), from which we developed algorithms to remove noise, and detect breaths as envelopes of spectral peaks. Phase II: In a distinct validation cohort (n = 59), we tested the ability of acoustic algorithms to detect AHI < 15 vs AHI > 15 on PSG.

Results

Smartphone-recorded sound analyses detected the presence, absence, and types of breath sound. Phase I: In the derivation cohort, spectral analysis identified breaths and apneas with a c-statistic of 0.91, and loud obstruction sounds with c-statistic of 0.95 on receiver operating characteristic analyses, relative to adjudicated events. Phase II: In the validation cohort, automated acoustic analysis provided a c-statistic of 0.87 compared to whole-night PSG.

Conclusions

Ambient sounds recorded from a smartphone during sleep can identify apnea and abnormal breathing verified on PSG. Future studies should determine if this approach may facilitate early screening of SDB to identify at-risk patients for definitive diagnosis and therapy.

Clinical trials

NCT03288376; clinicaltrials.org.

SUBMITTER: Narayan S

PROVIDER: S-EPMC6534134 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography.

Narayan Sanjiv S Shivdare Priyanka P Niranjan Tharun T Williams Kathryn K Freudman Jon J Sehra Ruchir R

Sleep & breathing = Schlaf & Atmung 20180718 1

<h4>Purpose</h4>Diagnosis of obstructive sleep apnea by the gold-standard of polysomnography (PSG), or by home sleep testing (HST), requires numerous physical connections to the patient which may restrict use of these tools for early screening. We hypothesized that normal and disturbed breathing may be detected by a consumer smartphone without physical connections to the patient using novel algorithms to analyze ambient sound.<h4>Methods</h4>We studied 91 patients undergoing clinically indicated ...[more]

PMID: 30022325

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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:PurposeThe proliferation of smartphones, accompanied by internet facilities, has contributed to a decrease in sleep quality over the last decades. It has been revealed that excessive internet usage impacts the physical and mental health of smartphone users, while personality traits (PT) could play a role in developing internet addictions and preventing their negative effects. The objective of the present study is to assess the role of PT and smartphone usage in sleep quality.MethodThe sample comprised 269 participants, 55% females, within the age range of 15-64 years. We objectively collected one-week smartphone apps usage data from the participants. They also responded to demographics and the PT (BFI-10) questionnaires. The usage data of smartphone apps were processed to calculate smartphone usage amounts and sleep variables, including sleep duration, sleep distraction, sleeping time, and wake-up time. The data were analyzed using the correlation coefficient and regression analyses.ResultsThe results indicated that more smartphone usage was associated with reduced sleep duration, increased sleep distraction, and later bedtime. Furthermore, smartphone users with the conscientiousness trait had a longer sleep duration, earlier sleeping time, less sleep distraction, and earlier wakeablity. Sleep distraction was positively associated with openness. Extraversion and neuroticism were found to be positive predictors of early wakeablity. Neuroticism had a negative association with early wakeablity. Finally, the implications of the study have been discussed.ConclusionOur study's usage of data that was acquired objectively has strong methodological qualities. The present study is the first to contribute to the literature on the role of PT and objectively measured smartphone usage in the prediction of sleep quality. We found that smartphone use and sleep variables are associated with PT. Other scholars can use our dataset for benchmarking and future comparisons.

Project description:ImportanceConsumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.ObjectiveTo validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.Design, setting, and participantsThis diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.Main outcomes and measuresSensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.ResultsOf the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.Conclusions and relevanceThis diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Project description:BackgroundGreater awareness of sleep-disordered breathing and rising obesity rates have fueled demand for sleep studies. Sleep testing using level 3 portable devices may expedite diagnosis and reduce the costs associated with level 1 in-laboratory polysomnography. We sought to assess the diagnostic accuracy of level 3 testing compared with level 1 testing and to identify the appropriate patient population for each test.MethodsWe conducted a systematic review and meta-analysis of comparative studies of level 3 versus level 1 sleep tests in adults with suspected sleep-disordered breathing. We searched 3 research databases and grey literature sources for studies that reported on diagnostic accuracy parameters or disease management after diagnosis. Two reviewers screened the search results, selected potentially relevant studies and extracted data. We used a bivariate mixed-effects binary regression model to estimate summary diagnostic accuracy parameters.ResultsWe included 59 studies involving a total of 5026 evaluable patients (mostly patients suspected of having obstructive sleep apnea). Of these, 19 studies were included in the meta-analysis. The estimated area under the receiver operating characteristics curve was high, ranging between 0.85 and 0.99 across different levels of disease severity. Summary sensitivity ranged between 0.79 and 0.97, and summary specificity ranged between 0.60 and 0.93 across different apnea-hypopnea cut-offs. We saw no significant difference in the clinical management parameters between patients who underwent either test to receive their diagnosis.InterpretationLevel 3 portable devices showed good diagnostic performance compared with level 1 sleep tests in adult patients with a high pretest probability of moderate to severe obstructive sleep apnea and no unstable comorbidities. For patients suspected of having other types of sleep-disordered breathing or sleep disorders not related to breathing, level 1 testing remains the reference standard.

Dataset Information

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography.

Purpose

Methods

Results

Conclusions

Clinical trials

Publications

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography.

Similar Datasets

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