Automated Detection of Sleep Apnea Using Machine Learning: A Novel Approach Using Smartphone and Microphone for Breathing Sound Analysis

Abstract

In this study, we evaluate the accuracy of a novel setup in the detection of apneas and hypopneas and estimating the apnea-hypopnea index (AHI). The study device setup consists of a microphone placed underneath the nose and a smartphone to collect the data. We recruited patients who were referred to the St. Josephs Hamilton Sleep Clinic for a sleep study. Data from our study device is collected simultaneously with polysomnography (PSG) in the sleep lab. A total of 26 patients were recruited, of which 2 dropped out during the data collection. Data from the microphone was too noisy for interpretation in 3 patients. Across the remaining 21 patients, the AHI based on their PSG ranged from 2 to 125 events/h, with an average AHI of 34 events/h. We used regression models trained on microphone audio data to identify noise and we developed an algorithm based on root-mean-square of the audio data for automatic detection of apneas and hypopneas. With reference to the PSG, our study device had a sensitivity of 94% and specificity of 87% in detecting apneas/hypopneas across a cumulative 120.9 hours of sleep data and more than 3700 such events. Our study device was able to accurately predict the AHI to within 4.3 events/h (+/- 3.1). As such, we can conclude that our study device is accurate in identifying apneas/hypopneas, estimating the AHI and at ruling out cases of severe sleep apnea and can potentially be used as a screening test for this purpose. Our study device has the practical advantages of being very low cost and potentially more accessible as a screening tool, though further validation studies are needed to study accuracy across a larger population and for use at-home.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Hamilton Integrated Research Ethics Board of McMaster University gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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