Yıl 2019, Cilt 12 , Sayı 4, Sayfalar 333 - 342 2019-10-29

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi
Feature Selection for Obstructive Sleep Apnea Recognition

Mustafa SERT [1]


Obstrüktif uyku apnesi (OUA), uyku sırasında anormal nefes durması veya azalması ile sıkça tanımlanan yaygın bir uyku bozukluğudur. Bu hastalık, uyku ile ilgili düzensizlikler ya da kardiyovasküler hastalıklar gibi uzun vadeli sonuçlara yol açabilir. Bu çalışmada, uyku apnesi tanıma için, klinik polisomnografi (PSG) yöntemine alternatif olarak, çok kipli öznitelik kullanımı ve seçimine dayalı sayısal bir yöntem önerilmektedir. Önerilen yöntem, elektrokardiyogram (EKG) ve oksijen doygunluğu (SpO2) olarak adlandırılan iki fizyolojik sinyalin öznitelik düzeyli kaynaşımına dayalıdır. Her iki sinyal kaynağından da sağlam özellikler elde etmek ve öznitelik boyutunu azaltmak için Relieff, Chi-Square, Bilgi Kazancı (BK), Temel Bileşen Analizi (TBA) ve Kazanç Oranı (KO) olmak üzere beş öznitelik seçim yöntemi probleme uygulanmıştır. Elde edilen çok kipli öznitelikler ile Naive Bayes (NB), en yakın komşu (kNN) ve Destek Vektör Makinesi (DVM) sınıflandırıcıları tasarlanmış ve etkinlikleri sınanmıştır. PhysioNet veritabanındaki  gerçek örnekler üzerinde yapılan deneysel çalışmalar, önerilen yöntemin sınıflandırma başarımını artırdığını göstermektedir.

Obstructive sleep apnea (OSA) is a kind of sleep disorder and it is described by breathing irregularity during sleep. This disorder may lead to long-term consequences, such as sleep related irregularities and/or cardiovascular diseases. This paper proposes a multimodal and feature selection-based processing pipeline to detect OSA as a computer-based alternative way to clinical polysomnography (PSG) method. In the proposed method, the oxygen saturation (SpO2) and the electrocardiogram (ECG) signals are fused at the feature-level for the classification. Five feature selection methods, namely Relieff, Chi-Square, Information Gain (IG), Principal Component Analysis (PCA), and Gain Ratio (GR) were applied to the problem to obtain robust features from both signal sources and to reduce the feature dimensionality. The effectiveness of utilized feature selection methods was analyzed using the Support Vector Machine (SVM), k-nearest neighbor (k-NN), and Naive Bayes (NB) classifiers. The experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and feature selection-based method improves the classification accuracy, significantly.

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Birincil Dil en
Konular Bilgisayar Bilimleri, Bilgi Sistemleri
Bölüm Makaleler
Yazarlar

Orcid: 0000-0002-7056-4245
Yazar: Mustafa SERT (Sorumlu Yazar)
Kurum: Baskent University
Ülke: Turkey


Destekleyen Kurum Başkent University
Teşekkür The author thank Gökhan Memiş for running the experiments.
Tarihler

Yayımlanma Tarihi : 29 Ekim 2019

Bibtex @araştırma makalesi { gazibtd615014, journal = {Bilişim Teknolojileri Dergisi}, issn = {1307-9697}, eissn = {2147-0715}, address = {}, publisher = {Gazi Üniversitesi}, year = {2019}, volume = {12}, pages = {333 - 342}, doi = {10.17671/gazibtd.615014}, title = {Feature Selection for Obstructive Sleep Apnea Recognition}, key = {cite}, author = {SERT, Mustafa} }
APA SERT, M . (2019). Feature Selection for Obstructive Sleep Apnea Recognition. Bilişim Teknolojileri Dergisi , 12 (4) , 333-342 . DOI: 10.17671/gazibtd.615014
MLA SERT, M . "Feature Selection for Obstructive Sleep Apnea Recognition". Bilişim Teknolojileri Dergisi 12 (2019 ): 333-342 <https://dergipark.org.tr/tr/pub/gazibtd/issue/49914/615014>
Chicago SERT, M . "Feature Selection for Obstructive Sleep Apnea Recognition". Bilişim Teknolojileri Dergisi 12 (2019 ): 333-342
RIS TY - JOUR T1 - Feature Selection for Obstructive Sleep Apnea Recognition AU - Mustafa SERT Y1 - 2019 PY - 2019 N1 - doi: 10.17671/gazibtd.615014 DO - 10.17671/gazibtd.615014 T2 - Bilişim Teknolojileri Dergisi JF - Journal JO - JOR SP - 333 EP - 342 VL - 12 IS - 4 SN - 1307-9697-2147-0715 M3 - doi: 10.17671/gazibtd.615014 UR - https://doi.org/10.17671/gazibtd.615014 Y2 - 2019 ER -
EndNote %0 Bilişim Teknolojileri Dergisi Feature Selection for Obstructive Sleep Apnea Recognition %A Mustafa SERT %T Feature Selection for Obstructive Sleep Apnea Recognition %D 2019 %J Bilişim Teknolojileri Dergisi %P 1307-9697-2147-0715 %V 12 %N 4 %R doi: 10.17671/gazibtd.615014 %U 10.17671/gazibtd.615014
ISNAD SERT, Mustafa . "Feature Selection for Obstructive Sleep Apnea Recognition". Bilişim Teknolojileri Dergisi 12 / 4 (Ekim 2019): 333-342 . https://doi.org/10.17671/gazibtd.615014
AMA SERT M . Feature Selection for Obstructive Sleep Apnea Recognition. Bilişim Teknolojileri Dergisi. 2019; 12(4): 333-342.
Vancouver SERT M . Feature Selection for Obstructive Sleep Apnea Recognition. Bilişim Teknolojileri Dergisi. 2019; 12(4): 342-333.