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Sleep apnea classification based on respiration signals by using ensemble methods

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Avcı, Cafer; * | Akbaş, Ahmet

Affiliations: Department of Computer Engineering, Yalova University, 77200, Yalova, Turkey

Correspondence: [*] Address for correspondence: Cafer Avcı, Department of Computer Engineering, Yalova University, 77200, Yalova, Turkey. Tel.: +90 226 815 5342; Fax: +90 226 815 5401; E-mail: [email protected].

Keywords: Sleep apnea, respiratory signals, wavelet, ensemble, boosting, bagging

DOI: 10.3233/BME-151470

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1703-S1710, 2015

Published: 2015
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In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal component analysis. The features obtained from 8 recordings are used for the classification sleep apnea by using three ensemble classifiers. According to the results, the classification accuracies have been obtained between 92.07-98.43%, 92.75-98.68% and 92.42-98.61% by using three different ensemble classifier based on abdominal, chest and nasal based analysis, respectively for AdaBoost, Random Forest and Random Subspace. However the best result is obtained analyzing nasal based respiratory signal by using Random Forest method. In this case accuracy is 98.68%.

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