Automatic Stage Scoring of Single-Channel Sleep EEG using CEEMD of Genetic Algorithm and Neural Network

Using an intelligent method to automatically detect sleep patterns in medical applications is one of the most important challenges in recent years to reduce the workload of physicians in analyzing sleep data through visual inspection. In this paper, a single-channel EEG-based algorithm is presented for automatic recognition of sleep stages using complete ensemble empirical mode decomposition and combined model of genetic algorithm and neural network. The signal is decomposed into IMFs using the complete ensemble empirical mode decomposition and statistical properties of each of the inherent state functions are extracted. In order to optimize and reduce the dimension of the feature vectors, a hybrid model of genetic algorithm and multi-layer propagation neural network is used. Then, McNemar's test is used to confirm the accuracy of the selected features. The final classification is performed on these optimized properties by a perceptron neural network with a hidden layer. On the average, classification accuracy of 98.9%, 97.1%, 96.7%, 94.8% and 93.8% are obtained respectively for 2, 3, 4, 5 and 6 classes with corresponding Kappa cohen coefficients of 0.98, 0.95, 0.95, 0.83 and 0.90. The results prove that the proposed sleep stage classification method has better performance compared to the previously existing methods.