Feature Selection for Obstructive Sleep Apnea Recognition

Year 2019, Volume: 12 Issue: 4, 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

References

• W. W. Flemons, D. Buysse, S. Redline, A. Oack, K. Strohl, J. Wheatley, T. Young, N. Douglas, P. Levy, W. McNicolas, J. Fleetham, D. White, W. Schmidt-Nowarra, D. Carley, J. Romaniuk, “Sleep-related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research”, Sleep, 22(5), 667-689, 1999.
• A. Zarei, B. M. Asl, “Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal”, in IEEE Journal of Biomedical and Health Informatics, 23(3), 1011-1021, 2019.
• B. M. Altevogt, H. R. Colten, Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem, Institute of Medicine (US) Committee on Sleep Medicine and Research, Washington (DC): National Academies Press (US), 2006.
• D. Leger, “The cost of sleep-related accidents: a report for the National Commission on Sleep Disorders Research”, Sleep, 17(1), 84-93, 2994.
• J. N. McNames, A. M. Fraser, “Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram”, Computers in Cardiology, 27, 749-752, 2000.
• F. Mendonca, S. S. Mostafa, A. G. Ravelo-Garca, F. Morgado-Dias, T. Penzel, “Review of Obstructive Sleep Apnea Detection Approaches,” in IEEE Journal of Biomedical and Health Informatics, 23(2), 825-837, 2019.
• L. Lavie, “Obstructive sleep apnoea syndrome an oxidative stress disorder,” Sleep Medicine Reviews, 7(1), 35-51, 2003.
• M.O. Mendez, S. Cerutti, A.M. Bianchi, J. Corthout, S. Van Huffel, M. Matteucci, T. Penzel, “Automatic Screening of Obstructive Sleep Apnea from the ECG Based on Empirical Mode Decomposition and Wavelet Analysis”, Physiological Measurement, 31(3), 273-289, 2010.
• R.K. Kakkar, R.B. Berry, “Positive Airway Pressure Treatment for Obstructive Sleep Apnea”, Chest., 132(3), 1057-1072, 2007.
• C. Armon, K. G. Johnson, A. Roy, W. J. Nowack, “Polysomnography”, 2016.
• P. Chazal, T. Penzel, C. Heneghan, “Automated Detection of Obstructive Sleep Apnoea at Different Time Scales Using the Electrocardiogram”, Physiological Measurement, 25, 967-983, 2004.
• T. Penzel, J. McNames, P. de Chazal, B. Raymond, A. Murray, G. Moody, “Systematic Comparison of Different Algorithms for Apnea Detection Based on Electrocardiogram Recordings”, Medical and Biological Engr. and Comp., 40(4), 402-407, 2002.
• V. Vimala, K. Ramar, M. Ettappan, “An Intelligent Sleep Apnea Classification System Based on EEG Signals”, Journal of medical systems, 43(2), 36, 2019, https://doi.org/10.1007/s10916-018-1146-8.
• B. Yilmaz, M. H. Asyali, E. Arikan, S. Yetkin, F. Ozgen, “Sleep Stage and Obstructive Apneaic Epoch Classification using Single-lead ECG”, BioMedical Engineering, 9(1), 39, 2010.
• F. Espinoza-Cuadros, R. Fernndez-Pozo, D. T. Toledano et al., “Reviewing the connection between speech and obstructive sleep apnea”, BioMedical Engineering, 15, 20, 2016.
• T.M. Rutkowski, “Data Driven Multimodal Sleep Apnea Events Detection”, Journal of Medical Systems, 40, 162, 2016.
• A. H. Khandoker, M. Palaniswami, C. K. Karmakar, “Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings”, in IEEE Transactions on Information Tech. in Biomedicine, 13(1), 37-48, 2009.
• R.B. Shouldice, L.M. O'Brien, C. O'Brien, P. de Chazal, D. Gozal, C. Heneghan, “Detection of Obstructive Sleep Apnea in Pediatric Subjects using Surface Lead Electrocardiogram Features”, Sleep, 27(4), 784-792, 2004.
• E. Urtnasan, J. U. Park, E. Y. Joo, K. J. Lee, “Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network”, Journal of medical systems, 42(6), 104, 2018, https://doi.org/10.1007/s10916-018-0963-0.
• P. de Chazal, C. Heneghan, E. Sheridan, R. Reilly, P. Nolan, M. O'Malley, “Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea”, in IEEE Tran. on Biomedical Engineering, 50(6), 686-696, 2003.
• M. O. Mendez, A. M. Bianchi, M. Matteucci, S. Cerutti, T. Penzel, “Sleep Apnea Screening by Autoregressive Models From a Single ECG Lead”, in IEEE Transactions on Biomedical Engineering, 56(12), 2838-2850, 2009.
• M. Bsoul, H. Minn, L. Tamil, “Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG”, in IEEE Transactions on Information Technology in Biomedicine, 15(3), 416-427, 2011.
• N. Oliver, F. Flores-Mangas, “HealthGear: a real-time wearable system for monitoring and analyzing physiological signals”, InternationalWorkshop onWearable and Implantable Body Sensor Networks (BSN'06), Cambridge, MA, 4-64, 2006.
• B. Raymond, R. M. Cayton, R. A. Bates, M. Chappell, “Screening for obstructive sleep apnoea based on the electrocardiogram-the computers in cardiology challenge”, Computers in Cardiology 2000, 27 (Cat. 00CH37163), 267-270, Cambridge, MA, 2000.
• Fu-Chung Yen, K. Behbehani, E. A. Lucas, J. R. Burk, J. R. Axe, “Noninvasive technique for detecting obstructive and central sleep apnea”, in IEEE Tran. on Biom. Engr., 44(12), 1262-1268, 1997.
• A. Patangay, P. Vemuri, A. Tewfik, “Monitoring of Obstructive Sleep Apnea in Heart Failure Patients”, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, 1043-1046, 2007.
• B. Xie, H. Minn, “Real-Time Sleep Apnea Detection by Classifier Combination”, in IEEE Transactions on Information Technology in Biomedicine, 16(3), 469-477, 2012.
• L. F. Chen, C. T. Su, K. H. Chen et al, “Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis”, Neural Computing and Applications, 21(8), 2087-2096, 2012.
• J. L. Blanco, L. A. Hernndez, R. Fernndez et al., “Improving Automatic Detection of Obstructive Sleep Apnea Through Nonlinear Analysis of Sustained Speech”, Cognitive Computation, 5(4), 458-472, 2015.
• O. Aydogan, A. Oter, K. Guney et al., “Automatic Diagnosis of Obstructive Sleep Apnea/Hypopnea Events Using Respiratory Signals”, Journal of Medical Systems, 40, 274, 2016.
• M. K. Ucar, M. R. Bozkurt, C. Bilgin, K. Polat, “Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques”, Neural Computing and Applications, 28(10), 2931-2945, 2016.
• H. Lee, J. Park, H. Kim et al., “New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal”, Journal of Medical Systems, 40, 282, 2016.
• J. Kim, T. Kim, D. Lee et al., “Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification”, BioMedical Engineering, 16, 6, 2017.
• D. Dey, S. Chaudhuri, S. Munshi, “Obstructive sleep apnoea detection using convolutional neural network based deep learning framework”, Biomedical engineering letters, 8(1), 95-100, 2018, https://doi.org/10.1007/s13534-017-0055-y.
• S. M. Islam, H. Mahmood, A. A. Al-Jumaily, S. Claxton, “Deep Learning of Facial Depth Maps for Obstructive Sleep Apnea Prediction”, 2018 International Conference on Machine Learning and Data Engineering (iCMLDE), Sydney, Australia, 154-157, 2018.
• S. M. Isa, M. I. Fanany, W. Jatmiko, A. M. Arymurthy, “Sleep Apnea Detection from ECG Signal: Analysis on Optimal Features, Principal Components, and Nonlinearity”, 2011 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 1-4, 2011.
• N. Xiong, P. Svensson, “Multi-sensor management for information fusion: issues and approaches”, Information Fusion, 3(2), 163-186, 2002.
• I. Kononenko, E. Simec, M. R. Sikonja, “Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF”, Applied Intelligence, 7(1), 39-55, 1997.
• J. R. Quinlan, “Induction of decision trees,” Machine Learning, 1(1), 81-106, 1986.
• Thomas M. Mitchell, Machine Learning, 1 ed., McGraw-Hill, Inc., New York, NY, USA, 1997.
• M. Robnik-Sikonja, I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF”, Machine Learning, 53(1), 23-69, 2003.
• I. Jolliffe, “Principal Component Analysis”, International Encyclopedia of Statistical Science, Editör: Lovric M., Springer, Berlin, Heidelberg , 2011.
• R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, Wiley Interscience, 2000.
• M. H. Calp, “Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(4), 6-16, 2018.
• T. B. Alakuş, İ. Türkoğlu, “Pozitif ve Negatif Duyguların Ayrımında Etkili EEG Kanallarının Dalgacık Dönüşümü ve Destek Vektör Makineleri ile Belirlenmesi”, Bilişim Teknolojileri Dergisi, 12(3), 229-237, 2019.
• C.C Chang, C.J. Lin, “LIBSVM: a library for support vector machines”, ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27, 2011.
• A. L. Goldberger, L. Amaral.et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”, Circulation, 101(23), e215-e220, 2000.
• C. Shi, M. Nourani, G. Gupta, L. Tamil, “Apnea MedAssist II: A smart phone based system for sleep apnea assessment”, 2013 IEEE Intl. Conf. on Bioinformatics and Biomedicine, Shanghai, 572-577, 2013.
• G. Memis, M. Sert, “Multimodal Classification of Obstructive Sleep Apnea Using Feature Level Fusion”, 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, 85-88, 2017.