ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG, EOG, AND EMG EXTRACTED FEATURES

Figures & data

TABLE 1 Available Systems Reported in Literature for Sleep Stage Detection

Authors	Classified stages	Classifier approach	Results
Watanabe and Watanabe (2004)	– Non-REM stages– REM stage– Wake	Mathematical modeling	82.6%38.3%70.5%
Šušmáková and Krakovská (2008)	Wake and four sleep stages	Fractal exponent and fractal dimension	Classification error 23%
Zoubek et al. (2007)	Sleep/Wake	Supervised classification methods (Bayes rule-based classifiers and Multi-layer Perceptron)	About 80%
Pinero et al. (2004)	Wake, REM and four sleep stages	Fuzzy model	78–84%
Flexer, Gruber, and Dorffner (2005)	Wakefulness, deep, and REM sleep	Hidden Markov model	Around 80%
Louis, Lee, and Stephenson (2004)	– Wake and Sleep– Wake, REM and NREM	Computer based steps algorithm	90.1%87.5%
Fraiwan, Khaswaneh, and Lweesy (2009)	Wake, REM and four sleep stages	Wavelet packets decomposition	About 74%
Tagluk, Sezgin, and Akin (2009)	REM and four sleep stages	Artificial Neural Networks	About 74.7%
Saastamoinen et al. (2006)	– Wake– Light sleep– Deep sleep– REM	Computer based spectrum approach with two parameters^+: window length & band frequency.	69–76.6%^+41–43.1%^+78–93.1%^+44.8–51.4%^+

FIGURE 1 The overall system architecture.

FIGURE 2 Different recorded EEG, EOG, and EMG epochs corresponding to sleep stages: a – W, b – 1, c – 2, d – 3, e – 4, and f – R.

TABLE 2 Frequency Range for EEG Events and Waves

Event	Frequency range (Hz)
K-complex	0.4–1.5
Delta	0.5–4
Theta	4–8
Alpha	8–13
Sleep spindle	12–14
Beta	13–25

TABLE 3 Ranked Attributes Based on Entropy

Entropy	Attributes	Entropy	Attributes	Entropy	Attributes
0.1415	LPC2A	0.1140	TotalPowerB	0.0815	EMGStd
0.1415	LPC2B	0.1017	ThetaA	0.0810	AlphaB
0.1398	DeltaA	0.0968	EOGRP2	0.0783	LPC4B
0.1367	BetaA	0.0912	AlphaA	0.0783	LPC4A
0.1322	DeltaB	0.0906	SpindleA	0.0649	EOGLP1
0.1317	LPC3B	0.0899	KComplexB	0.0520	EMGVar
0.1317	LPC3A	0.0896	KComplexA	0.0519	EOGRP1
0.1259	BetaB	0.0868	ThetaB	0	LPC1B
0.1238	TotalPowerA	0.0868	EOGLP2	0	LPC1A
0.1199	EOGCorr	0.0818	SpindleB	–	–

FIGURE 3 The ANFIS architecture of each sleep stage.

FIGURE 4 Initial membership functions of the five inputs for the stage-W subsystem.

FIGURE 5 Membership functions for the five inputs of stage W subsystem after training.

TABLE 4 Features for the ANFIS Subsystem Structure for Each Stage

Number of nodes:	2096
Number of linear parameters:	1024
Number of nonlinear parameters:	60
Total number of parameters:	1084
Number of training data pairs:	9266
Number of fuzzy rules:	1024
Number of layers:	5
Input layer nodes:	5
Output layer nodes:	1
Training approach:	Hybrid algorithm(combines the least square method with the gradient descent method)

FIGURE 6 Overall accuracy using two, three, and four membership functions (MF) to represent the input features.

FIGURE 7 Sensitivity, selectivity, specificity and accuracy for sleep stages subsystems using two, three and four membership functions (MF) to represent the input features.

TABLE 5 The Performance of the System Using Two Membership Functions

Number of MF = 2	Stage W	Stage 1	Stage 2	Stage 3	Stage 4	Stage R
Sensitivity (%)	95.88	89.74	87.79	92.74	96.94	95.08
Selectivity (%)	92.93	95.15	86.65	100.0	90.03	95.29
Specificity (%)	91.65	94.84	83.93	100.0	87.66	94.92
Accuracy (%)	93.77	92.29	85.86	96.37	92.30	95.00
Overall accuracy (%)	89.37	85.82	77.34	92.74	87.55	90.82

TABLE 6 The Performance of the System Using Three Membership Functions

Number of MF = 3	Stage W	Stage 1	Stage 2	Stage 3	Stage 4	Stage R
Sensitivity (%)	95.96	90.68	89.86	92.86	96.22	95.96
Selectivity (%)	93.62	94.75	88.20	99.84	91.63	97.15
Specificity (%)	92.46	94.36	85.53	99.85	89.71	96.90
Accuracy (%)	94.21	92.52	87.70	96.36	92.97	96.43
Overall accuracy (%)	90.07	86.35	80.22	92.74	88.44	93.33

TABLE 7 The Performance of the System Using Four Membership Functions

Number of MF = 4	Stage W	Stage 1	Stage 2	Stage 3	Stage 4	Stage R
Sensitivity (%)	95.77	91.35	90.06	93.13	96.15	96.26
Selectivity (%)	94.76	94.54	89.05	99.72	92.04	97.18
Specificity (%)	93.83	94.14	86.55	99.72	90.23	96.92
Accuracy (%)	94.80	92.75	88.31	96.43	93.19	96.59
Overall accuracy (%)	90.96	86.81	81.08	92.89	88.78	93.64

TABLE 8 The Performance of the System

	Stage W	Stage 1	Stage 2	Stage 3	Stage 4	Stage R
Sensitivity (%)	92.09	83.73	81.95	94.66	97.19	89.79
Selectivity (%)	61.92	66.38	67.54	60.81	59.81	63.00
Specificity (%)	53.76	64.98	56.93	60.72	55.76	59.61
Accuracy (%)	85.65	74.36	69.44	77.69	76.48	74.70

Watanabe , T. , and K. Watanabe . 2004 . Noncontact method for sleep stage estimation . IEEE Transactions on Biomedical Engineering 51 ( 10 ): 1735 – 1748 .

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Šušmáková , K. , and A. Krakovská . 2008 . Discrimination ability of individual measures used in sleep stages classification . Artificial Intelligence in Medicine 44 ( 3 ): 261 – 277 .

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Zoubek , L. , S. Charbonnier , S. Lesecq , A. Buguet , and F. Chapotot . 2007 . Feature selection for sleep/wake stages classification using data driven methods . Biomedical Signal Processing and Control 2 ( 3 ): 171 – 179 .

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Pinero , P. , P. Garcia , L. Arco , A. Alvarez , M. Garcia , and R. Bonal . 2004 . Sleep stage classification using fuzzy sets and machine learning techniques . Neurocomputing 58 – 60 : 1137–1143 .

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Flexer , A. , G. Gruber , and G. Dorffner . 2005 . A reliable probabilistic sleep stager based on a single EEG signal . Artificial Intelligence in Medicine 33 ( 3 ): 199 – 207 .

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Louis , R. P. , J. Lee , and R. Stephenson . 2004 . Design and validation of a computer-based sleep-scoring algorithm . Journal of Neuroscience Methods 133 ( 1–2 ): 71 – 80 .

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Saastamoinen , A. , E. Huupponen , A. Värri , J. Hasan , and S. Himanen . 2006 . Computer program for automated sleep depth estimation . Computer Methods and Programs in Biomedicine 82 ( 1 ): 58 – 66 .

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