Sleep Stages Classification Using Neural Network with Single Channel EEG
نویسندگان
چکیده
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
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