Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezoelectric Bands

Yin Yan Lin, Hau Tieng Wu, Chi An Hsu, Po Chiun Huang, Yuan Hao Huang, Yu Lun Lo

Research output: Contribution to journalJournal Article peer-review

52 Scopus citations

Abstract

Physiologically, the thoracic (THO) and abdominal (ABD) movement signals, captured using wearable piezoelectric bands, provide information about various types of apnea, including central sleep apnea (CSA) and obstructive sleep apnea (OSA). However, the use of piezoelectric wearables in detecting sleep apnea events has been seldom explored in the literature. This study explored the possibility of identifying sleep apnea events, including OSA and CSA, by solely analyzing one or both the THO and ABD signals. An adaptive nonharmonic model was introduced to model the THO and ABD signals, which allows us to design features for sleep apnea events. To confirm the suitability of the extracted features, a support vector machine was applied to classify three categories - normal and hypopnea, OSA, and CSA. According to a database of 34 subjects, the overall classification accuracies were on average 75.9%± 11.7% and 73.8%± 4.4%, respectively, based on the cross validation. When the features determined from the THO and ABD signals were combined, the overall classification accuracy became 81.8%± 9.4%. These features were applied for designing a state machine for online apnea event detection. Two event-by-event accuracy indexes, S and I, were proposed for evaluating the performance of the state machine. For the same database, the S index was 84.01%± 9.06% and the I index was 77.21%± 19.01%. The results indicate the considerable potential of applying the proposed algorithm to clinical examinations for both screening and homecare purposes.

Original languageEnglish
Article number7776756
Pages (from-to)1533-1545
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume21
Issue number6
DOIs
StatePublished - 11 2017

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Abdominal movement signal
  • adaptive nonharmonic model
  • breathing pattern variability
  • sleep apnea
  • synchrosqueezing transform
  • thoracic movement signal

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