Abstract
This study mainly focuses on
automatic sleep stage classification based on polysomnographic sleep recordings
obtained from obstructive sleep apnea subjects. Various studies have so far
classified sleep stages based on EEG recordings obtained from normal subjects.
Because obstructive sleep apnea subjects’ sleep is often interrupted throughout
the night, accurate scoring of their sleep disorders is important for
diagnosis. The signals for automatic sleep stages classification were selected
in accordance with American Academy of Sleep Medicine criteria. Feature vectors
consisting of these signal power values for the automatic sleep stage
classification were calculated as inputs of ANN (Artificial Neural Networks). We re-ordered the feature
vector table obtained from signals via the algorithm developed to increase the
success of the ANN. In this study, training and testing success of ANN were
determined by using 10-fold cross-validation. In the study of automatic sleep
stage scoring implemented by ANN, the correct recognition rate of Wakefulness,
REM (Rapid Eye Movement), NREM1(Non REM1), NREM2, NREM3 were found as 95%, 93%,
91%, 86% and 92%, respectively. The findings suggest that training and test
success of automatic sleep stage classification are better compared to the
other studies in the literature.
References
- [1] HORI T, KOGA E., SHIRAKAWA S., INOUE K., UCHIDA S., KUWAHARA H., KOUSAKA M., KOBAYASHI T., TSUJI Y., TERASHIMA M., FUKUDA K., FUKUDA, N, “Proposed supplements and amendments to A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, the Rechtschaffen & Kales (1968) standard”, Psychiatry and Clinical Neurosciences, vol. 55, p. 305–310, 2001.
- [2] R.B. BERRY, R. BROOKS, C.E. GAMALDO, S.M. HARDING, C. MARCUS, B. VAUGHN, FOR THE AMERICAN ACADEMY OF SLEEP MEDICINE. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.3. American Academy of Sleep Medicine, Westchester, IL, 2016.
- [3] HSU Y.L, YANG Y.T, WANG J. S, HSU C.-Y, “Automatic sleep stage recurrent neural classifier using energy features of EEG signals”, Neurocomputing, vol. 104 p.105–114, 2013.
- [4] KÖKTÜRK O, “Scoring of Sleep Recordings, Turkish Respiratory Society”, Journal of Respiration, 15, 14-29, 2013.
- [5] KILINÇ O., BAYRAM H., Obstructive Sleep Apnea Syndrome Diagnosis and Treatment Convention Report, Journal of the Turkish Thoracic Society, volume 13, 2012.
- [6] VIRKKALA J, HASAN J, VARRI A, HIMANEN S.-L, MULLER K, “A Automatic sleep stage classification using two-channel electro-oculography”, Journal of Neuroscience Methods vol. 166, p.109–115, 2007.
- [7] HUUPPONEN E, KULKAS A, SAASTAMOINEN A, TENHUNEN M, HIMANEN S. L, “Identification of Deep Sleep and Awake with Computational EEG Measures”, Journal Medical Systems, v: 35, p. 1413–1420, 2011.
- [8] FRAIWAN L, LWEESY K, KHASAWNEH N, WENZ H, DICKHAUS H, “Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier”, Computer methods and programs in biomedicine vol.108, p. 10–19, 2012.
- [9] FAKHRA S. M, TORBATIA M.M, HILL M, HILL C. M, WHITE P. R, “Signal processing techniques applied to human sleep EEG signals—A review”, Biomedical Signal Processing and Control vol.10, p. 21–33, 2014.
- [10] STEPNOWSKY C, LEVENDOWSKI D, POPOVIC D, AYAPPA I, RAPOPORT D. M, “Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters”, Sleep Medicine vol.14, p. 1199–1207, 2013.
- [11] ESTÉVEZ D.A., FERNANDEZ- J.M. HERNÁNDEZ E., -BONILLO V.M., “A method for the automatic analysis of the sleep macrostructure in continuum”, Expert Systems with Applications 40(5), 2012.
- [12] DUMAN F, Identification of Sleep Condition by Analysing EEG Signals, Masters Thesis, Ankara University, Department of Electronic Engineering, 2005.
- [13] ERDAMAR A, DUMAN F., S. YETKIN S., “A wavelet and teager energy operator based method for automatic detection of K Complex in sleep EEG”, Expert Systems with Applications, 39 (1), 1284–1290, 2004.
- [14] KIYMIK M. K, AKIN M, SUBASI. A, “Automatic recognition of alertness level by using wavelet transform and artificial neural network”, Journal of Neuroscience Methods, vol. 139 p. 231–240, 2004.
- [15] KOHAVI R, A study of cross validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on artificial intelligence pp. 1137–1143, 2002.
- [16] EBRAHIMI F, MIKAEILI M, ESTRADA E, NAZERAN H, “Automatic Sleep Stage Classification Based on EEG Signals by Using Neural Networks and Wavelet Packet Coefficients”, 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008.
- [17] JORDAN T. J, Understanding medical information: A user’s guide to informatics and decision making. New York: McGraw-Hill, 2002.
- [18] PAPOULIS A., Probability, Random Variables, and Stochastic Processes, McGraw-Hill International Editions, 1991.
Abstract
Bu çalışmada, Tıkayıcı uyku apnesi sahip
kişilerden elde edilen polisomnografik uyku kayıtlarına dayanan otomatik uyku
evresi sınıflandırma çalışması yapılmıştır. Çeşitli çalışmalarda, normal
kişilerden elde edilen EEG kayıtlarına dayanarak uyku evreleri
sınıflandırılmıştır. Tıkayıcı uyku apneli kişilerin uykusu gece boyunca
sıklıkla kesintiye uğradığından, uyku bozukluklarının doğru skorlanması tanı
için önemlidir. Otomatik uyku evrelerinin sınıflandırılması için sinyaller
Amerikan Uyku Tıbbı Akademisi kriterlerine göre seçilmiştir. Otomatik uyku
evrelerinin sınıflandırması için bu sinyal gücü değerlerinden oluşan özellik
vektörleri, ANN (Yapay Sinir Ağları) girdileri olarak hesaplanmıştır. YSA'nın
başarısını artırmak için geliştirilen algoritma ile sinyallerden elde edilen
özellik vektör tablosunu yeniden sıralanmıştır. Bu çalışmada, YSA'nın eğitim ve
test başarısı 10 kat çapraz doğrulama kullanılarak belirlenmiştir. YSA tarafından uygulanan otomatik uyku evre
skorlaması çalışmasında, Uyanıklık, REM (Hızlı Göz Hareketi), NREM1 (Hızlı göz
hareki olmayan), NREM2, NREM3'ün doğru tanıma oranı sırasıyla %95, % 93, % 91,
% 86 ve % 92 olarak bulunmuştur. Bulgular, otomatik uyku evresi sınıflandırma
eğitim ve test başarısının literatürdeki diğer çalışmalara göre daha iyi
olduğunu göstermektedir.
References
- [1] HORI T, KOGA E., SHIRAKAWA S., INOUE K., UCHIDA S., KUWAHARA H., KOUSAKA M., KOBAYASHI T., TSUJI Y., TERASHIMA M., FUKUDA K., FUKUDA, N, “Proposed supplements and amendments to A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, the Rechtschaffen & Kales (1968) standard”, Psychiatry and Clinical Neurosciences, vol. 55, p. 305–310, 2001.
- [2] R.B. BERRY, R. BROOKS, C.E. GAMALDO, S.M. HARDING, C. MARCUS, B. VAUGHN, FOR THE AMERICAN ACADEMY OF SLEEP MEDICINE. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.3. American Academy of Sleep Medicine, Westchester, IL, 2016.
- [3] HSU Y.L, YANG Y.T, WANG J. S, HSU C.-Y, “Automatic sleep stage recurrent neural classifier using energy features of EEG signals”, Neurocomputing, vol. 104 p.105–114, 2013.
- [4] KÖKTÜRK O, “Scoring of Sleep Recordings, Turkish Respiratory Society”, Journal of Respiration, 15, 14-29, 2013.
- [5] KILINÇ O., BAYRAM H., Obstructive Sleep Apnea Syndrome Diagnosis and Treatment Convention Report, Journal of the Turkish Thoracic Society, volume 13, 2012.
- [6] VIRKKALA J, HASAN J, VARRI A, HIMANEN S.-L, MULLER K, “A Automatic sleep stage classification using two-channel electro-oculography”, Journal of Neuroscience Methods vol. 166, p.109–115, 2007.
- [7] HUUPPONEN E, KULKAS A, SAASTAMOINEN A, TENHUNEN M, HIMANEN S. L, “Identification of Deep Sleep and Awake with Computational EEG Measures”, Journal Medical Systems, v: 35, p. 1413–1420, 2011.
- [8] FRAIWAN L, LWEESY K, KHASAWNEH N, WENZ H, DICKHAUS H, “Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier”, Computer methods and programs in biomedicine vol.108, p. 10–19, 2012.
- [9] FAKHRA S. M, TORBATIA M.M, HILL M, HILL C. M, WHITE P. R, “Signal processing techniques applied to human sleep EEG signals—A review”, Biomedical Signal Processing and Control vol.10, p. 21–33, 2014.
- [10] STEPNOWSKY C, LEVENDOWSKI D, POPOVIC D, AYAPPA I, RAPOPORT D. M, “Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters”, Sleep Medicine vol.14, p. 1199–1207, 2013.
- [11] ESTÉVEZ D.A., FERNANDEZ- J.M. HERNÁNDEZ E., -BONILLO V.M., “A method for the automatic analysis of the sleep macrostructure in continuum”, Expert Systems with Applications 40(5), 2012.
- [12] DUMAN F, Identification of Sleep Condition by Analysing EEG Signals, Masters Thesis, Ankara University, Department of Electronic Engineering, 2005.
- [13] ERDAMAR A, DUMAN F., S. YETKIN S., “A wavelet and teager energy operator based method for automatic detection of K Complex in sleep EEG”, Expert Systems with Applications, 39 (1), 1284–1290, 2004.
- [14] KIYMIK M. K, AKIN M, SUBASI. A, “Automatic recognition of alertness level by using wavelet transform and artificial neural network”, Journal of Neuroscience Methods, vol. 139 p. 231–240, 2004.
- [15] KOHAVI R, A study of cross validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on artificial intelligence pp. 1137–1143, 2002.
- [16] EBRAHIMI F, MIKAEILI M, ESTRADA E, NAZERAN H, “Automatic Sleep Stage Classification Based on EEG Signals by Using Neural Networks and Wavelet Packet Coefficients”, 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008.
- [17] JORDAN T. J, Understanding medical information: A user’s guide to informatics and decision making. New York: McGraw-Hill, 2002.
- [18] PAPOULIS A., Probability, Random Variables, and Stochastic Processes, McGraw-Hill International Editions, 1991.
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Engineering |
| Journal Section | Electrical and Electronics Engineering |
| Authors | |
| Publication Date | January 28, 2019 |
| Submission Date | May 15, 2018 |
| Acceptance Date | September 27, 2018 |
| Published in Issue | Year 2019 Volume: 8 Issue: 1 |
