Sleep Stages Classification Using Neural Network with Single Channel EEG

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International Journal of Recent Advances in Engineering & Technology (IJRAET)

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ISSN (Online): 2347 - 2812, Volume-2, Issue -8, 2014 5

Sleep Stages Classification Using Neural Network with Single Channel EEG

1K. Venkatesh, ²S. Poonguzhali, ³K. Mohanavelu, ⁴K. Adalarasu

1,2,3

Dept of ECE, college of engineering, Guindy, Anna university, chennai.

4Defence Bio-engineering and Electromedical Laboratory, Bangalore.

5 Dept of ECE, PSNA College of Engineering and Technology, Dindigul.

Email: [email protected],

Abstract - The usual method for sleep stages classification is visual inspection method by sleep specialist. It uses eight EEG channels (O1, O2, T3, T4, C3, C4, Fp1 and Fp2), EOG and also EMG for sleep analysis. This method consumes more time (hours) for sleep stages classification.

Some brain disorders like narcolepsy (excessive day time sleepiness) requires real-time monitoring of sleep states which is not possible by using conventional techniques.

Hence sleep stages classification is done using artificial neural network (ANN). Feature parameters such as minimum amplitude, maximum amplitude, mean, standard deviation and energy of delta, theta, alpha and beta of each sleep stage were extracted using discrete wavelet transform (DWT). These feature for training and also for testing ANN, results obtained with this technique is accurate and also less time consuming as compare to other techniques Keywords: artificial neural network (ANN), discrete wavelet transform (DWT). Electroencephalogram (EEG), Feed forward neural network (FNN).

I. INTRODUCTION

The importance of sleep analysis is both in medicine and in theoretical area. There are many sleep disorders, e.g., the most frequent are insomnia, narcolepsy, sleep apnoea; many other disorders manifest themselves through sleep disturbances (e.g. depression, schizophrenia Alzheimer disease, etc.). After the pain, sleep disturbances are the second most indicator of illness.

According to Rechtschaffen and kales (1968) sleep states consist of two general stages: rapid eye movement (REM) sleep and non rapid eye movement (NREM) sleep [3]. NREM is in turn subdivided into four stages:

1, 2, 3, and 4. During the whole night sleep of a human five different sleep stages will occurs starting from awake ( drowsiness) to stage 4, totally 3-4 cycles of sleep stages occurs. During the first cycle the duration of deep sleep will be more as the night progresses the duration will get decreases but REM sleep (dream sleep) duration will get increases [4].

The usual method for sleep stage classification is based on visual inspection method by a sleep specialist. In this method it needs large data for sleep analysis that is eight EEG channels, EMG, and EOG [6]. Since EMG and EOG shows lot of variation for different sleep stages. It is more time consuming technique and also real time sleep monitoring cannot be achieved by the subject with respect to more number of electrodes. Some brain disorders like narcolepsy (excessive day time sleepiness), requires real-time monitoring of sleep states which is not possible using conventional techniques [6].

Hence researchers had been done sleep stages classification with different neural network classifiers like back propagation, Learning vector Quantization, Probabilistic Neural Networks and Feed Forward Neural network using features like total energy, spectral energy, mean, standard deviation, ratio of sub band energies.

And the electrode channels (Fpz-Cz) used in the experiment was selected from centre lobe of the brain as sleep cycle is produced by Pons and also Pz-Oz channel is used. The techniques used for extracting the features; wavelet packets, harmonic Hjorth methods [2]

and Hilbert-Huang transform [7]. But the results obtained by the classifiers for some stages like stage 1 was around 78% and for stage 2 was 75%.

Hence it is proposed that in this technique using five different features (minimum amplitude, maximum amplitude, mean, standard deviation and energy) of each delta, theta, alpha and beta frequency bands were used for classification. The features were extracted using wavelet decomposition technique; the classification of five different sleep stages is done using FNN and results obtained with this technique compared with the conventional techniques.

In the proposed technique the C3-O1 channel is used for feature extraction since the sleep cycles regulate by Pons which is placed in the central core of the brain.

And FNN is used for classification since it uses scaled conjugate gradient back propagation algorithm for

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weights updating. Hence neural network can achieve quick learning rate.

The variation of frequency bands of EEG with respect to each sleep stage is explained in the table1 and wave patterns are shown in fugure1.

Table1. Sleep stages of EEG (K. Susmakova, 2004) S.

no

Sleep Stage

Description

1 awake Low voltage (10-30µv), continuity of alpha activity 2 Stage1 Low voltage, mixed frequency

EEG with highest amplitude in 2-7 Hz range

3 Stage2 Sleep spindles and k-complexes occurs. K-complex is a sharpe negative wave followed by a ppositive one. Sleep spindles occurs in 12-14 Hz range.

4 Stage3 20%-50% delta activity 5 Stage4 More than 50% of delta activity 6 REM Low voltage and mixed

frequency. Sawtooth wave pattern is often present, similar to stage1.

Figure 1: Wave pattern of different sleep stages (K.

Susmakova, 2004).

II. MATERIALS AND METHODS

A. Data Collection:

The recorded sleep EEG data from ten male subjects, average of 45 years age is taken from physionet database (MIT-BIH polysomnographic database). The sleep subjects were free from psychiatric and neurological problems. From the seven channel

polysomnographic database signals single channel (C3-O1) sleep EEG signal had been is selected, the sampling rate of the signal was 250 Hz. And the sleep stages (awake, stage 1, stage 2, stage 3, stage 4 and REM) were separated from the sleep EEG signals using the annotations given in the physionet database.

B. Discrete Wavelet Transform

EEG is the electrical pattern records on the surface of the brain formed by the aggregate of synchronized neural activities from millions of neurons acting together. In most case, it has been showed that EEG is a classical non-stationary signal. Short time Fourier transforms (STFT) was applied to analyze EEG signals which were a time-frequency analysis method, but it has been noted that the short time Fourier transform depends critically on the choice of the window (Sun et al., 2006).

Wavelet transform brings a solution to this problem which is a multi-resolution analysis method and could give a more accurate temporal localization. Wavelet transform is a new two dimensional time-scale processing method for non-stationary signals (Mallat, 1989). Its main advantage is to provide simultaneous information on frequency and time location of the signal characteristics in terms of the representation of the signal at multiple resolutions corresponding to different time scales.

The sampling rate of the sleep EEG was 250 Hz. Hence a five level decomposition is applied on the signal using daubechies wavelet of order 4 (db4). Using wavelet decomposition technique, four frequency bands (delta, theta, alpha and beta) of sleep EEG were extracted;

these bands were represented by the approximated and detailed coefficients of wavelet decomposition as shown in the table 2.

Table 2: frequency and rhythm representation of approximated and detailed coefficients

Wavelet coefficients

Frequency range (Hz)

rhythm

CA5 0-3.9 delta

CD5 3.9-7.8 theta

CD4 7.8-15.6 alpha

CD3 15.6-31.2 beta

C. Feature Extraction

From all the ten sleep subjects‟ five different features like minimum amplitude, maximum amplitude, mean, standard deviation and energy were extracted for all the frequency bands (delta, theta, theta and beta) of each sleep stage from the 30 seconds duration of the sleep EEG. All the stages of sleep signal were different from each other so the extracted features were shown lot of variation especially energy since all frequency bands of sleep EEG were different in its rhythm. The mathematical formula for mean, standard deviation and energy are given as follows

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Mean= ¹

𝑛 ^𝑛_𝑖=1𝑋𝑖

Standard Deviation =[ ¹₂ ^𝑛_𝑖=1(𝑋𝑖 −𝑋𝑖 )²]¹² Energy = ^𝑛_𝑖=1(𝑋𝑖)²

Where „X‟ represents sleep EEG signal.

D. Artificial Neural Network

Artificial neural network is used as classifier since it is easy to design and also given better accuracy with less time. A three layered (input, hidden, output) Feed forward neural network (FNN) is used in this study with 41 neurons in the hidden layer. The number of neurons in the hidden layer was calculated using Kolmogorov‟s theorem. FNN is used as a classifier since it uses scaled conjugate gradient back propagation algorithm for weights updation [9] .The quick learning is possible using conjugate gradient algorithm compare to back propagation algorithm. The number of 30 seconds segments used from all the sleep stages of ten sleep subjects is shown in table 3. The five different features of each band were extracted from all the 30 sec segments of ten sleep subjects. FNN was trained, tested and also validation check had been done with the extracted features. The architecture of three layered FNN with the 20 neurons in the input layer, 41 neurons in the hidden layer and 5 neurons in the output layer is shown in figure 3. All the neurons in the input layer are connected to the every neuron of hidden layer and also from hidden layer all the neurons are connected to the all the neurons of output layer. During the FNN training the weights were updated based on error value generated for each iteration. The FNN was trained with faster learning rate and also less mean square value is achieved.

Input layer hidden layer output layer Fig3: FNN for sleep stage classification E. Classification & Results:

The five features are calculated for four bands of each stage of sleep EEG, for 30sec duration. The features were extracted for each and every sleep stage of all nine

subjects. The FNN is trained and also tested with the extracted features, the number of 30 sec epochs used from all the sleep stages of ten subjects and the results obtained with the FNN is given in table 3.

Table 3: number of epochs used from all sleep stages and FNN accuracy for each stage

Stage 30sec scored epochs

FNN accuracy

awake 250 95%

Stage1 275 85%

Stage2 550 85.33%

SWS 350 90.33%

REM 400 95%

III. DISCUSSION AND CONCLUSION

The combination of sleep stage 3 and stage 4 named as slow wave stage (SWS) and also called as deep sleep.

Hence features extracted from these two stages were combined and classified as SWS stage. Some of the features were similar for stage 1 and stage 2. Hence classifier accuracy was poor for these stages. Among all the bands the energy value for delta band had been shown more variation with respect to each sleep stage.

The future work is to design better neural network and also to test the network with more number of subjects for better accuracy.

REFERENCES

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[10] http://www.physionet.org/physiobank/database/slp db/

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