TY - GEN
T1 - Uyku Apnesi Siniflandirma Performansini Geliştirmek İçin Çok Kipli ve Öznitelik Seçimine Dayali Bir Yaklaşim
AU - Memis, Gokhan
AU - Sert, Mustafa
AU - Yazici, Adnan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/27
Y1 - 2017/6/27
N2 - Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust features from both biological signals and design three classifiers, namely Naive Bayes (NB), k-nearest neighbors (kNN), and Support Vector Machine (DVM) to test these features. Our experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and Relieff feature selection based method improves the average classification accuracy by 4.67% on all test scenarios.
AB - Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust features from both biological signals and design three classifiers, namely Naive Bayes (NB), k-nearest neighbors (kNN), and Support Vector Machine (DVM) to test these features. Our experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and Relieff feature selection based method improves the average classification accuracy by 4.67% on all test scenarios.
KW - Electrocardiogram (ECG)
KW - Obstructive Sleep Apnea (OSA) classification
KW - Relieff Feature Selection
KW - Saturation of Peripheral Oxygen (SpO2)
KW - Support Vector Machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85026325025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026325025&partnerID=8YFLogxK
U2 - 10.1109/SIU.2017.7960547
DO - 10.1109/SIU.2017.7960547
M3 - Conference contribution
AN - SCOPUS:85026325025
T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017
Y2 - 15 May 2017 through 18 May 2017
ER -
