S-Space Graduate School of Convergence Science and Technology (융합과학기술대학원) Dept. of Transdisciplinary Studies(융합과학부) Journal Papers (저널논문_융합과학부)
Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification
Cited 11 time in Web of Science Cited 11 time in Scopus
- Issue Date
- BioMedical Engineering OnLine, 16(1):6
- Obstructive sleep apnea ; Breathing sound ; OSA severity classification ; Transition probability ; Cyclostationary ; Apnea-hypopnea index
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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 patients breathing sounds.
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.
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.
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.
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