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Sleep and Breathing

, Volume 23, Issue 1, pp 269–279 | Cite as

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

  • Sanjiv NarayanEmail author
  • Priyanka Shivdare
  • Tharun Niranjan
  • Kathryn Williams
  • Jon Freudman
  • Ruchir Sehra
Sleep Breathing Physiology and Disorders • Original Article

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

Keywords

Sleep screening Smartphone App Sleep apnea Sleep-disordered breathing Polysomnography Sound Signal processing Fourier transform 

Abbreviations

AHI

Apnea-hypopnea index

BMI

Body mass index (kg/m2)

CPAP

Continuous positive airway pressure

dB

Decibel

ECG

Electrocardiogram

EEG

Electroencephalogram

EMG

Electromyogram

FFT

Fast Fourier transform

HST

Home sleep testing

PSG

Polysomnography

RMS

Root-mean-square

ROC

Receiver operating characteristic

SD

Standard deviation

SDB

Sleep-disordered breathing

Notes

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.

Ethical approval

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

Informed consent was obtained from all individual participants included in the study. No identifying information from participants was recorded.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sanjiv Narayan
    • 1
    Email author
  • Priyanka Shivdare
    • 1
  • Tharun Niranjan
    • 1
  • Kathryn Williams
    • 2
  • Jon Freudman
    • 1
  • Ruchir Sehra
    • 1
  1. 1.Resonea Inc.ScottsdaleUSA
  2. 2.Mayo ClinicScottsdaleUSA

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