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Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier.
Tuesday,2013-08-13 18:36 America/Los_Angeles — eenright
| Title | Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier. |
| Publication Type | Journal Article |
| Year of Publication | 2012 |
| Authors | Al-Angari, HM, Sahakian, AV |
| Journal | IEEE Trans Inf Technol Biomed |
| Volume | 16 |
| Issue | 3 |
| Pagination | 463-8 |
| Date Published | 2012 May |
| ISSN | 1558-0032 |
| Keywords | Adolescent, Adult, Case-Control Studies, Child, Heart Rate, Humans, Middle Aged, Oximetry, Oxygen, Pattern Recognition, Automated, Polysomnography, Respiratory Rate, Sleep Apnea, Obstructive, Support Vector Machine |
| Abstract | <p>Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.</p> |
| DOI | 10.1109/TITB.2012.2185809 |
| Alternate Journal | IEEE Trans Inf Technol Biomed |
| PubMed ID | 22287247 |
| PubMed Central ID | PMC4487628 |
| Grant List | U01 HL080295 / HL / NHLBI NIH HHS / United States HHSN268200800007C / HL / NHLBI NIH HHS / United States N01HC55222 / HL / NHLBI NIH HHS / United States N01HC85086 / HL / NHLBI NIH HHS / United States HHSN268201200036C / HL / NHLBI NIH HHS / United States N01HC85082 / HL / NHLBI NIH HHS / United States N01HC85083 / HL / NHLBI NIH HHS / United States N01HC85079 / HL / NHLBI NIH HHS / United States R01 AG023629 / AG / NIA NIH HHS / United States N01HC85080 / HL / NHLBI NIH HHS / United States N01HC85081 / HL / NHLBI NIH HHS / United States |