Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.


This Document Has Been Replaced By:



This Document Has Been Retired


Up-to-date Information

This is the latest update:

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography
  • Published Date:

    July 18 2018

  • Source:
    Sleep Breath. 23(1):269-279
  • Language:
Filetype[PDF-1.77 MB]

  • Alternative Title:
    Sleep Breath
  • Description:
    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;
  • Pubmed ID:
  • Pubmed Central ID:
  • Document Type:
  • Collection(s):
  • Main Document Checksum:
  • File Type:
No Related Documents.

You May Also Like: