Performance Comparison of Standard Polysomnographic Parameters Used in the Diagnosis of Sleep Apnea

Year 2024, Volume: 19 Issue: 1, 257 - 263, 28.03.2024

Seda Arslan Tuncer , Yakup Çiçek , Taner Tuncer

https://doi.org/10.55525/tjst.1419740

Abstract

Obstructive sleep apnea (OSAS), which is one of the leading sleep disorders and can result in death if not diagnosed and treated early, is most often confused with snoring. OSAS disease, the prevalence of which varies between 0.9% and 1.9% in Turkey, is a serious health problem that occurs as a result of complete or partial obstruction of the respiratory tract during sleep, resulting in sleep disruption, poor quality sleep, paralysis and even death in sleep. Polysomnography signal recordings (PSG) obtained from sleep laboratories are used for the diagnosis of OSAS, which is related to factors such as the individual's age, gender, neck diameter, smoking-alcohol consumption, and the occurrence of other sleep disorders. Polysomnography is used in the diagnosis and treatment of sleep disorders such as snoring, sleep apnea, parasomnia (abnormal behaviors during sleep), narcolepsy (sleep attacks that develop during the day) and restless legs syndrome. It allows recording various parameters such as brain waves, eye movements, heart and chest activity measurement, respiratory activities, and the amount of oxygen in the blood with the help of electrodes placed in different parts of the patient's body during night sleep. In this article, the performance of PSG signal data for the diagnosis of sleep apnea was examined on the basis of both signal parameters and the method used. First, feature extraction was made from PSG signals, then the feature vector was classified with Artificial Neural Networks, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN) and Logistic Regression (LR).

Keywords

Sleep, Sleep Apnea, PSG, Classification

Uyku Apnesinin Teşhisinde Kullanılan Standart Polisomnografik Parametrelerin Performans Karşılaştırılması

Abstract

Erken tanı konulmadığında ve tedavi edilmediği zaman ölümle sonuçlanabilen ve uyku hastalıklarının başından gelen Tıkayıcı uyku apnesi (OSAS) en çok horlama ile karıştırılmaktadır. Türkiye’de görülme yaygınlığı %0,9 ila %1,9 değişen OSAS hastalığı uyku süresi boyunca solunum yollarnın tamamen veya kısmen tıkanması sonucunda görülen uyku bölünmesi, kalitesiz uyku geçirme, felç olma ve hatta uykuda ölümün bile görülmesi gibi sonuçlar doğuran ciddi bir sağlık sorunudur. Bireyin yaşı, cinsiyeti boyun çapı, sigara-alkol tüketimi, diğer uyku rahatsızlıkların görülme durumu gibi etmenlerle ilişkili olan OSAS tanı için uyku laboratuvarlarından alınan Polisomnografi sinyal kayıtları (PSG) kullanılmaktadır. Polisomnografi horlama, uyku apnesi, parasomnia (uyku esnasında anormal davranışlar), narkolepsi (gün içinde gelişen uyku atakları), huzursuz bacak sendromu gibi uyku bozukluklarının tanı ve tedavisinde kullanılır. Gece uykusu boyunca hasta vücudunun farklı bölgelerine yerleştirilen elektrotlar yardımıyla beyin dalgaları, göz hareketleri, kalp ve göğüs aktivitesinin ölçülmesi, solunum etkinlikleri, kandaki oksijen miktarı gibi çeşitli parametrelerin kayıt altına alınmasını sağlar. Bu makalede, PSG sinyal verilerinin uyku apnesinin teşhisine yönelik başarımları hem sinyal parametreleri hem de kullanılan yöntem bazında incelendi. İlk olarak PSG sinyallerinden özellik çıkartımı yapıldı daha sonra özellik vektörü yapay sinir ağları, destek vektör makinesi (DVM), k-enyakın komşu (k-NN) ve lojistik regrasyon (LR) ile sınıflandırıldı.

Keywords

Uyku, Uyku Apnesi, PSG, Sınıflandırma

References

• Akılotu BN, Tuncer SA. OSAS Evaluation By Means Of Machine Learning And Artificial Neural Networks By Using Polisomnographic Report Data, International Conference on Engineering Technologies (ICENTE’17), 2017.
• Demir A, et al. Türk Toraks Derneği Obstrüktif Uyku Apne Sendromu Tanı Ve Tedavi Uzlaşı Raporu”, Türk Toraks Dergisi, Cilt 13, Vol.13, 2012.
• Xıe J, Yu W, Wan Z, Han F, Wang Q, Chen R. Correlation Analysis between Obstructive Sleep Apnea Syndrome (OSAS) and Heart Rate Variability, Iran J Public Health., 46(11), p:1502–1511, 2017.
• Marcos C, Hornero JVR, Álvarez D, Nabney IT. Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis, Med Biol Eng Comput ., 48(9):895-902, 2010.
• Liu D, Pang Z, Lloyd SR. A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG, IEEE Transactions on Neural Networks, 19(2), 308-318, 2008.
• Akhter S, Abeyratne UR, Swarnkar V. Characterization of REM/NREM sleep using breath sounds in OSA, Biomedical Signal Processing and Control, 25, 130-142, 2016.
• Sharma M, Agarwal S, Acharya UR. Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals, Computers in Biology and Medicine, vol.100, 100-113, 2018.
• Jane R, Sola-Soler J, Fiz JA, Morera J. Automatic detection of snoring signals: validation with simple snorers and OSAS patients, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 2000
• Kunyang L, Weifeng P, Yifan L, Qing J, Guanzheng L. A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal, Neurocomputing, Vol.294, 94-101, 2018.
• Banluesombatkul N, Rakthanmanon T, Rapaport TW. Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach, TENCON 2018 - pp. 2011-2016, 2018.
• Akılotu BN, Tuncer SA. Evaluation of the Effect of CPAP Device on REM Sleep in OSAS Patients Using YSA and SVM, International Conference on Engineering Technologies”, (ICENTE’17), Dec 07-09, Konya, Turkey, 2017.
• Khandoker AH, Palaniswami M, Karmakar CK. Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings, IEEE transactions on information technology in biomedicine, 13(1),37-48, 2009.
• Almazaydeh L, Faezipour M, Elleithy K. A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features, International Journal of Advanced Computer Science and Applications, 3(5), 7-11, 2012.
• Lin SY, Wu Y, Mao W, Wang P. EEG signal analysis of patients with obstructive sleep apnea syndrome (OSAS) using power spectrum and fuzzy entropy, 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China, pp. 740-744, 2017.
• Karandikar K, Le T, Sa-ngasoongsong A, Wongdhamma W, Bukkapatnam S. Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals, Annu Int Conf IEEE Eng Med Biol Soc., 7088-91, 2013.
• Mostafa SS, Mendonça F, Morgado-Dias F, Ravelo-García A. SpO2 based sleep apnea detection using deep learning, IEEE 21st International Conference on Intelligent Engineering Systems (INES), Larnaca, Cyprus, 2017.
• Jezzini A, Ayache M, Elkhansa L, Ibrahim Z. ECG classification for sleep apnea detection, 2015 International Conference on Advances in Biomedical Engineering (ICABME), Beirut, Lebanon, pp. 301-304, 2015.
• Lee, E., Lee, H. Clinical and Polysomnographic Characteristics of Adult Patients with Suspected OSAS from Different Sleep Clinics at a Single Tertiary Center. Neurol Ther, 2024.
• Chien, W.-C. Et.al. The Associations between Polysomnographic Parameters and Memory Impairment among Patients with Obstructive Sleep Apnea: A 10-Year Hospital-Based Longitudinal Study. Biomedicines, 11, 621, 2023.
• M. Gasa, et al., Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach, Archivos de Bronconeumología Vol. 59. Issue 10., pages 638-644 , 2023.
• E.Ç. Edis, et.al. Polysomnography findings and risk factors for sleep-disordered breathing in patients with systemic sclerosis, Archives of Rheumatology, 36(3), 2021.
• Zhou SJ, et. al. Measuring Sleep Stages and Screening for Obstructive Sleep Apnea with a Wearable Multi-Sensor System in Comparison to Polysomnography”, Nat Sci Sleep., 15:353-362, 2023.
• Garg VK, Bansal RK, Intelligent Computing Techniques for the Detection of Sleep Disorders: A Review, International Journal of Computer Applications, 110, 0975 – 8887,1, 2015.
• Alvarez-Estevez D, Moret-Bonillo V. Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review, Sleep Disorders, 2015:237878, 2015.
• Motamedi-Fakhr S et. al. Signal processing techniques applied to human sleep EEG signals-A review, Biomedical Signal Processing and Control. 10, 21–33, 2014.