Abstract
Recently, sleep disorder is taken as a serious issue in people living. Normally people cerebrum passes through variety of static physiological steps or changes for the duration of sleep. Biomedical signal such as EEG, ECG, EOG and EMG setup and signals used to recognize sleep disorders. This work proposes better technique that can be designed to discriminate the stages of sleep which can help physicians to do an analysis and examination of related sleep disorders. In order to identify a modification inside brain, EEG signal partitioned with 5 frequency bands: delta, theta, alpha, beta and gamma. After signal acquisition, Band pass filter is applied to discriminate the input EEG signal of Fpz–Cz electrodes into frequency bands. Statistical specific features are extracted from distinctiveness impression of EEG signal. Then classification is required for classifying the sleep stages automatically with fuzzy kernel support vector machine and simple recurrent network (SRN). In SRN, statistical features were extracted and allocate 30 s period to 5 possible levels in sleep; wakefulness, Non Rapid Eye Movement Sleep Stage 1 (NREMSS 1), NREMSS 2, NREMSS 3 and NREMSS 4, Rapid Eye Movement Sleep Stage (REMSS). These signal acquired from sleep-EDF repository from PhysioBank (PB) used to validate our proposed scheme. Simple recurrent network classification performance rate is found as 90.2% than that of other new classifiers such as feed forward neural network (FNN) and probabilistic neural network (PNN) next it was compared and results are experimented in proposed work.
Similar content being viewed by others
Change history
11 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04315-9
References
Agarwal R, Gotman J (2001) Computer-assisted sleep staging. IEEE Trans Biomed Eng 48(12):1412–1423
Alickovic E, Subasi A (2018) Ensemble SVM method for automatic sleep stage classification. IEEE Instrum Meas 67:1–8
Belousov AI, Verzakov SA, von Frese J (2002) A flexible classification approach with optimal generalization performance: support vector machines. Chemo Metric Sand Intell Lab Syst 64:15–25
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27
Charbonnier S, Zoubek L, Lesecq S, Chapotot F (2011) Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging. Comput Biol Med 41(6):380–389
De-xiang Z, Xiao-pei W, Xiao-jing G (2008) The EEG signal preprocessing based on empirical mode decomposition. In: 2nd International Conference on bioinformatics and biomedical engineering, ICBBE 2008, pp 2131–2134, 2008
Feldkamp LA, Puskorius GV (1998) A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification. In: Proc. IEEE 86 Vol 11, pp 2259–2277
Flexer A, Gruber G, Dorffner G (2005) A reliable probabilistic sleep stager based on a single EEG signal. Artif Intell Med 33(3):199–207
Fraiwan LA, Khaswaneh NY, Lweesy KY (2009) Automatic sleep stage scoring with wavelet packets based on single EEG recording. World Acad Sci Eng Technol 54:485–488
Funahashi KI, Nakamura Y (1993) Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw 6(6):801–806
Guler NF, Ubeyli ED, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 3:506–514
Hanaoka M, Kobayashi M, Yamazaki H (2002) Automatic sleep stage scoring based on waveform recognition method and decision-tree learning. Syst Comput Jpn 33(11):1–13
Hassan AR, Mohammed IHB (2016) A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features. J Neurosci Methods 271:107–118
Hsu YL, Yang YT, Wang JS, Hsu CY (2013) Automatic sleep stage recurrent neural classifier using energy features of EEG signals. J Neurocomput 104:105–114
Husken Michael, Stage Peter (2003) Recurrent neural networks for time series classification. Neuro Comput 50:223–235
Jiang D, Lu Y-N, Yu MA, Yuanyuan W (2019) “Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement. Expert Syst Appl 121:188–203
Jin L, Nikiforuk PN, Gupta MM (1995) Approximation of discrete-time state-space trajectories using dynamic current neural networks. IEEE Trans Autom Control 40(7):1266–1270
Kemp B (2000) The Sleep-EDF Database. http://www.physionet.org/physiobank/database/sleep-edf/S
Lin C, Wang S (2004) Training algorithms for fuzzy support vector machines with noisy data. Pattern Recogn Lett 25:1647–1656
Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063–2079. https://doi.org/10.1109/TNNLS.2018.2790388
Oropesa E, Cycon HL, Jobert M (1999) Sleep stage classification using wavelet transform and neural networks. International Computer Science Institute. ICSI Technical Report TR-99-008
Sabeti M, Katebi S, Boostani R (2009) Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med 47(3):263–274
Schaltenbrand N, Lengelle R, Toussaint M, Luthringer R, Carelli G, Jacqmin A, Lainey E, Muzet A, Macher JP (1996) Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. Sleep 19(1):26–35
Srinivasan A, Sadagopan S (2020) Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01672-w
Stanus E, Lacroix B, Kerkhofs M, Mendlewicz J (2003) Automated sleep scoring: a comparative reliability study of algorithms, Electroencephalogram. Clin Neuro-physiol 66(1987):448–456
van Sweden B, Kemp B, Kamphuisen HAC, Vander Velde EA (1990) Alternative electrode placement in(automatic)sleep scoring(Fpz_Cz/Pz_Oz versus C4-A1). Sleep 13:279–283
Venkatachalam K, Devipriya A, Maniraj J, Sivaram M, Ambikapathy A, Amiri IS (2020) A novel method of motor imagery classification using eeg signal”. J Artif Intell Med Elsevier 103:101787
Yasoda K, Ponmagal RS, Bhuvaneshwari KS, Venkatachalam K (2020) Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA). Soft Comput J. https://doi.org/10.1007/s00500-020-04920-w
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04315-9
About this article
Cite this article
Basha, A.J., Balaji, B.S., Poornima, S. et al. RETRACTED ARTICLE: Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel. J Ambient Intell Human Comput 12, 6189–6197 (2021). https://doi.org/10.1007/s12652-020-02188-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02188-4