Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network

Current methods for sleep stage detection rely on sensors to collect physiological data. These methods are inaccurate and take up considerable medical resources. Thus, in this study, we propose a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model to automatically detect and classify sleep stages. In the proposed T-MCCFNN model, multiscale convolution kernels extract features of the input electroencephalogram signal and a compensatory fuzzy neural network is used in place of a traditional fully connected network as a classifier to improve the convergence rate during learning and to reduce the number of model parameters required. Due to the complexity of general deep learning networks, trial and error methods are often used to determine their parameters. However, this method is very time-consuming. Therefore, this study uses the Taguchi method instead, where the optimal parameter combination is identified over a minimal number of experiments. We use the Sleep-EDF database to evaluate the proposed model. The results indicate that the proposed T-MCCFNN sleep stage classification accuracy is 85.3%, which is superior to methods proposed by other scholars.

Automated summary

In this study, a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model is proposed for automatic detection and classification of sleep stages. The experimental results show that the proposed model achieves a sleep stage classification accuracy of 85.3%, which is superior to methods proposed by other scholars.