title: Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification.
abstract: BACKGROUND
  Polysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patient's breathing sounds.
  
  
  METHODS
  Breathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects' sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation.
  
  
  RESULTS
  The overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5.
  
  
  CONCLUSIONS
  The results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds.
authors.affiliation: Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
authors.forename: Jaepil
authors.initials: J
authors.lastname: Kim
authors.affiliation: Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
authors.forename: Taehoon
authors.initials: T
authors.lastname: Kim
authors.affiliation: Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
authors.forename: Donmoon
authors.initials: D
authors.lastname: Lee
authors.affiliation: Department of Otorhinolaryngology, Seoul National University Bundang Hospital, Gumi-ro, Seongnam, 13620, Republic of Korea. kimemail@snu.ac.kr.
authors.forename: Jeong-Whun
authors.initials: JW
authors.lastname: Kim
authors.affiliation: Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea. kglee@snu.ac.kr.
authors.forename: Kyogu
authors.initials: K
authors.lastname: Lee
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography; D011336:Probability; D012135:Respiratory Sounds; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D013997:Time Factors
keywords: Apnea-hypopnea index; Breathing sound; Cyclostationary; OSA severity classification; Obstructive sleep apnea; Transition probability