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- W4286542909 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." @default.
- W4286542909 created "2022-07-22" @default.
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- W4286542909 date "2022-01-01" @default.
- W4286542909 modified "2023-10-14" @default.
- W4286542909 title "SleepFCN: A Fully Convolutional Deep Learning Framework for Sleep Stage Classification Using Single-Channel Electroencephalograms" @default.
- W4286542909 doi "https://doi.org/10.1109/tnsre.2022.3192988" @default.
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