Can we discriminate between apnea and hypopnea using audio signals?

M. Halevi, E. Dafna, Ariel Tarasiuk, Yaniv Zigel

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

Abstract

Obstructive sleep apnea (OSA) affects up to 14% of the population. OSA is characterized by recurrent apneas and hypopneas during sleep. The apnea-hypopnea index (AHI) is frequently used as a measure of OSA severity. In the current study, we explored the acoustic characteristics of hypopnea in order to distinguish it from apnea. We hypothesize that we can find audio-based features that can discriminate between apnea, hypopnea and normal breathing events. Whole night audio recordings were performed using a non-contact microphone on 44 subjects, simultaneously with the polysomnography study (PSG). Recordings were segmented into 2015 apnea, hypopnea, and normal breath events and were divided to design and validation groups. A classification system was built using a 3-class cubic-kernelled support vector machine (SVM) classifier. Its input is a 36-dimensional audio-based feature vector that was extracted from each event. Three-class accuracy rate using the hold-out method was 84.7%. A two-class model to separate apneic events (apneas and hypopneas) from normal breath exhibited accuracy rate of 94.7%. Here we show that it is possible to detect apneas or hypopneas from whole night audio signals. This might provide more insight about a patient's level of upper airway obstruction during sleep. This approach may be used for OSA severity screening and AHI estimation.

Original languageEnglish GB
Pages3211-3214
Number of pages4
DOIs
StatePublished - 13 Oct 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: 16 Aug 201620 Aug 2016

Conference

Conference38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/TerritoryUnited States
CityOrlando
Period16/08/1620/08/16

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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