SleepFCN: A Fully Convolutional Deep Learning Framework for Sleep Stage Classification Using Single-Channel Electroencephalograms.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Published Date:

Abstract

Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a gold standard method. In this paper, we introduce a novel fully convolutional neural network architecture (SleepFCN) to classify sleep stages into five classes using single-channel electroencephalograms (EEGs). The framework of SleepFCN includes two major parts for feature extraction and temporal sequence encoding namely multi-scale feature extraction (MSFE) and residual dilated causal convolutions (ResDC), respectively. These are then followed by convolutional layers of 1-sized kernels instead of dense layers to build the fully convolutional neural network. Due to the imbalance in the distribution of sleep stages, we incorporate a weight corresponding to the number of samples of each class in our loss function. We evaluated the performance of SleepFCN using the Sleep-EDF and SHHS datasets. Our experimental results show that the proposed method outperforms state-of-the-art works in both classification correctness and learning speed.

Authors

  • Narjes Goshtasbi
  • Reza Boostani
  • Saeid Sanei
    Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, UK. Electronic address: s.sanei@surrey.ac.uk.