Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography
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.
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.
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.
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.
KeywordsSleep screening Smartphone App Sleep apnea Sleep-disordered breathing Polysomnography Sound Signal processing Fourier transform
Body mass index (kg/m2)
Continuous positive airway pressure
Fast Fourier transform
Home sleep testing
Receiver operating characteristic
Compliance with ethical standards
Conflict of interest
Drs. Narayan and Sehra are co-authors of intellectual property licensed to Resonea Inc., and hold equity in a company that has invested in Resonea, Inc. Ms. Shivdare was an employee of Resonea Inc. at the time of this study. Mr. Niranjan is a current employee of Resonea Inc. Drs. Williams and Freudman are paid consultant to Resonea Inc.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study. No identifying information from participants was recorded.
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