Feature Selection for Obstructive Sleep Apnea Recognition

Year 2019, , 333 - 342, 29.10.2019

Mustafa Sert

https://doi.org/10.17671/gazibtd.615014

Cited By: 1

Abstract

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.

Keywords

electrocardiogram (ECG), sleep apnea recognition, saturation of oxygen (SpO2), feature fusion, SVM, NB, k-NN

Supporting Institution

Başkent University

Thanks

The author thank Gökhan Memiş for running the experiments.

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

Abstract

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.

Keywords

obstrüktif uyku apnesi tanıma, öznitelik kaynaştırma, EKG, SpO2, DVM, NB, k-NN

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