Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier
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...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2012-05, Vol.16 (3), p.463-468 |
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| description | 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. |
| doi_str_mv | 10.1109/TITB.2012.2185809 |
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M. ; Sahakian, A. V.</creator><creatorcontrib>Al-Angari, H. M. ; Sahakian, A. V.</creatorcontrib><description>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.</description><identifier>ISSN: 1089-7771</identifier><identifier>ISSN: 2168-2194</identifier><identifier>EISSN: 1558-0032</identifier><identifier>EISSN: 2168-2208</identifier><identifier>DOI: 10.1109/TITB.2012.2185809</identifier><identifier>PMID: 22287247</identifier><identifier>CODEN: ITIBFX</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accuracy ; Adolescent ; Adult ; Case-Control Studies ; Child ; Heart Rate - physiology ; Heart rate variability ; Humans ; Kernel ; Middle Aged ; obstructive sleep apnea (OSA) ; Oximetry ; Oxygen - blood ; oxygen saturation ; paradoxical breathing ; Pattern Recognition, Automated - methods ; Polynomials ; Polysomnography - methods ; respiratory efforts ; Respiratory Rate - physiology ; Respiratory system ; Sensitivity ; Sleep apnea ; Sleep Apnea, Obstructive - blood ; Sleep Apnea, Obstructive - physiopathology ; Sleep disorders ; Support Vector Machine ; Support vector machines ; support vector machines (SVM)</subject><ispartof>IEEE journal of biomedical and health informatics, 2012-05, Vol.16 (3), p.463-468</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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M.</creatorcontrib><creatorcontrib>Sahakian, A. V.</creatorcontrib><title>Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier</title><title>IEEE journal of biomedical and health informatics</title><addtitle>TITB</addtitle><addtitle>IEEE Trans Inf Technol Biomed</addtitle><description>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%). 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| ispartof | IEEE journal of biomedical and health informatics, 2012-05, Vol.16 (3), p.463-468 |
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| subjects | Accuracy Adolescent Adult Case-Control Studies Child Heart Rate - physiology Heart rate variability Humans Kernel Middle Aged obstructive sleep apnea (OSA) Oximetry Oxygen - blood oxygen saturation paradoxical breathing Pattern Recognition, Automated - methods Polynomials Polysomnography - methods respiratory efforts Respiratory Rate - physiology Respiratory system Sensitivity Sleep apnea Sleep Apnea, Obstructive - blood Sleep Apnea, Obstructive - physiopathology Sleep disorders Support Vector Machine Support vector machines support vector machines (SVM) |
| title | Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier |
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