Abstract
The sleeping stage classification plays an important role in the medical science because it helps the diagnosis of the mental health diseases. The conventional approach for performing the sleeping stage classification is based on the electroencephalograms (EEGs). However, it is worth noting that the EEGs reflect the brain activities. Nevertheless, the brain activities are very complicated even though the subjects are sleeping. Hence, performing the sleeping stage classification via the EEGs may yield the low classification accuracy. On the other hand, the electrooculograms (EOGs) are the voltages between the front eyes and the back eyes which are related to the eye ball movement. As it can directly reflect the various sleeping stages, it can achieve a higher classification accuracy. Therefore, this paper employs the two channel EOGs for performing the sleeping stage classification. The major contribution of this paper is to 1) employ the singular spectrum analysis (SSA) to exploit the latent intrinsic high dimensional dynamics of the one dimensional EOGs for performing the sleeping stage classification, 2) employ the approximate entropy as the features for performing the sleeping stage classification, and 3) assign the same features of different SSA components of different channels of the epochs of the EOGs into the same group and perform the principal component analysis (PCA) on each group of the feature vectors so that the properties of each type of the features are preserved. The results show that our proposed method yields the five sleeping stage classification accuracy at 93.73% and the sensitivity of the stage one non-rapid eye movement (S1) at 78.44%, which achieves the significant improvements compared to the existing methods. Therefore, our proposed method could be used to reduce the workload of the medical officers.
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References
Arzi A, Shedlesky L, Ben-Shaul M, Nasser K, Oksenberg A (2000) Ilana S Hairston and Noam Sobel, “Humans can learn new information during sleep.” Nat Neurosci 15(10):1460–1465
Mahowald MW, Schenck CH (2005) Insights from studying human sleep disorders. Nature 437(7063):1279–1285
Penzel T, Conradt R (2000) Computer based sleep recording and analysis. Sleep Med Rev 4(2):131–148
Moser D, Anderer P, Gruber G, Parapatics S, Loretz E, Boeck M, Kloesch G, Heller E, Schmidt A, Danker-Hopfe H, Saletu B, Zeitlhofer J, Dorffner G (2009) Sleep classification according to aasm and rechtschaffen & kales: effects on sleep scoring parameters. Sleep 32(2):139–149
Berry RB, Budhiraja R, Gottlieb DJ, Gozal D, Iber C, Kapur VK, Marcus CL, Mehra R, Parthasarathy S, Quan SF et al (2012) Rules for scoring respiratory events in sleep: update of the 2007 aasm manual for the scoring of sleep and associated events: deliberations of the sleep apnea definitions task force of the american academy of sleep medicine. J Clin Sleep Med 8(5):597–619
Hau-tieng Wu, Talmon R, Lo Y-L (2015) Assess sleep stage by modern signal processing techniques. IEEE Trans Biomed Eng 62(4):1159–1168
Iranzo A (2006) Jose Luis Molinuevo, Joan Santamara, Monica Serradell Bsc, Maria Jose Marti, Francesc Valldeoriola and Eduard Tolosa, “Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study.” THE Lancet Neurol 5(7):572–577
Liang S-F, Kuo C-E, Yu-Han Hu, Pan Y-H, Wang Y-H (2012) Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models. IEEE Trans Instrum Meas 61(6):1649–1657
Vural C, Yildiz M (2008) Determination of sleep stage separation ability of features extracted from EEG signals using principle component analysis. J Med Syst 34(1):83–89
Bulling A, Roggen D, Tröster G (2009) Wearable EOG goggles: eye-based interaction in everyday environments. In CHI’09 Extended Abstracts on Human Factors in Computing Systems, pp 3259–3264
Kuo C-E, Liang S-F, Lee Y-C, Cherng F-Y, Lin W-C, Chen P-Y, Liu Y-C, Shaw F-Z (2014) An EOG-Based automatic sleep scoring system and its related application in sleep environmental control. Int Conf Physiol Comput Syst Springer, Berlin Heidelberg 8908:71–88
Virkkala J, Hasan J, Värri A, Himanen S-L, Müller K (2007) Automatic sleep stage classification using two-channel electro-oculography. J Neurosci Methods 166(1):109–115
Md Mosheyur Rahman (2018) Mohammed Imamul Hassan Bhuiyan and Ahnaf Rashik Hassan, “Sleep stage classification using single-channel EOG.” Comput Biol Med 102:211–220
Ahnaf Rashik Hassan and Mohammed Imamul Hassan Bhuiyan (2016) Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 24:1–10
Xia B, Li Q, Jia J, Wang J, Chaudhary U, Ramos-Murguialday A, Birbaumer N (2015) Electrooculogram based sleep stage classification using deep belief network. In 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–5
Liang S-F, Kuo C-E, Lee Y-C, Lin W-C, Liu Y-C, Chen P-Y, Cherng F-Y, Shaw F-Z (2015) Development of an EOG-Based automatic sleep-monitoring eye mask. IEEE Trans Instrum Meas 64(11):2977–2985
Lin Y, Ling BWK, Xu N, Lam RWK, Ho CYF (2020) Effectiveness analysis of bio-electronic stimulation therapy to Parkinson’s diseases via joint singular spectrum analysis and discrete Fourier transform approach. Biomed Signal Process Control 62:102–131
Ortiz A, Martinez-Murcia FJ, Formoso MA, Luque JL, Sanchez A (2020) Dyslexia detection from EGG signals using SSA component correlation and convolutional neural networks. In International Conference on Hybrid Artificial Intelligence Systems. Springer, pp 655–664
Zhou X, Lei R, Ling BWK, Li C (2019) Joint empirical mode decomposition and singular spectrum analysis based pre-processing method for wearable non-invasive blood glucose estimation. In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), IEEE, pp 259–263
Maddirala AK, Shaik RA (2015) Removal of EMG artifacts from single channel EEG signal using singular spectrum analysis. In 2015 IEEE International Circuits and Systems Symposium (ICSyS), IEEE, pp 111–115
Zabalza J, Ren J, Marshall S (2014) Singular spectrum analysis for effective noise removal and improved data classification in hyperspectral imaging. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, pp 1–4
Zabalza J, Qing C, Yuen P, Sun G, Zhao H, Ren J (2018) Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging. J Franklin Inst 355:1733–1751
Dabbaghchian S, Ghaemmaghami MP, Aghagolzadeh A (2010) Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology. Pattern Recogn 42(4):1431–1440
Revathi Balasundaram and Gnanou Florence Sudha (2021) Retrieval performance analysis of multibiometric database using optimized multidimensional spectral hashing based indexing. J King Saud Univ Comput Inf Sci 33(1):110–117
Skubalska-Rafajlowicz E (2009) Neural networks with sigmoidal activation functions – dimension reduction using normal random projection. Nonlinear Anal Theory Methods Appl 71(12):1225–1263
Alvarez-Estevez D, Rijsman R (2021) Haaglanden medisch centrum sleep staging database (version 1.0. 1). PhysioNet
Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Phys D: Nonlinear Phenomena 110:43–50
Xu X, Liu X, Chen X (2006) The Cao method for determining the minimum embedding dimension of sea clutter. In 2006 CIE International Conference on Radar, IEEE, pp 1–4
Pincus, Steven M (1991) Approximate entropy as a measure of system complexity. Proceed Nat Acad Sci United States Am 88(6):2297–2301
Zhang G, Christensen R, Pesko J (2021) Parametric boostrap and objective bayesian testing for heteroscedastic one-way ANOVA. Statistics & Probability Letters 174:109095
Peterson LE (2009) K-nearest neighbor. Scholarpedia 4(2):1883
Breiman L (1996) Bagging prediction. Mach Learn, pp. 24. https://doi.org/10.1007/BF00058655
Hearst MA (1998) Support vector machines. IEEE Intelligent Syst Appl 13(4):18–28
Chih-Chung C, Chih-Jen L (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–39
Olesen AN, Christensen JAE, Sorensen HBD, Jennum PJ (2016) A noise-assisted data analysis method for automatic EOG-Based sleep stage classification using ensemble learning. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 3769–3772
Horne J (2013) Why REM sleep? Clues beyond the laboratory in a more challenging world. Biol Psychol 92:152–168
Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Trans Syst Man, Cybern Part A: Syst Humans 40(1):185–197
Acknowledgements
This paper was supported partly by the National Nature Science Foundation of China (no. U1701266, no. 61671163 and no. 62071128), the Team Project of the Education Ministry of the Guangdong Province (no. 2017KCXTD011), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (no. 501130144) and the Hong Kong Innovation and Technology Commission, Enterprise Support Scheme (no. S/E/070/17).
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Che, JH., Ling, B.WK. & Zhou, X. Singular spectrum analysis based sleeping stage classification via electrooculogram. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-18103-w
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DOI: https://doi.org/10.1007/s11042-023-18103-w