A Computationally Efficient Single-Channel EEG Sleep Stage Scoring Approach using Simple Structured CNN

Abstract

Automatic sleep stage classification has been a hot trend since hand-crafted feature engineering is highly inefficient. However, current studies of automatic sleep stage scoring focus more on designing complex neural network structures to improve the model performance while neglecting the model efficiency. This causes both lengthy training time and highly demanding hardware are needed for model training, which is not favorable for future industrial applications. This work proves the concept that the simple model, such as a shallow Convolutional Neural Network (CNN) combining the proper data processing techniques, can achieve a comparable model performance (overall accuracy of 79.0 %) to the complex model (overall accuracy of 74.9-82.0 %). The designed model in this work also significantly improves the model efficiency by reducing the number of learnable parameters in the neural network. This approach provides a new insight into automatic sleep stage scoring study as well as other deep learning studies that the data processing and the model design are equally important.