SleepFCN: a fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms

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Goshtasbi, N, Boostani, R and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2022. SleepFCN: a fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms. IEEE Transactions on Neural Systems and Rehabilitation Engineering. ISSN 1534-4320

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Official URL: https://doi.org/10.1109/tnsre.2022.3192988

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.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Creators:	Goshtasbi, N., Boostani, R. and Sanei, S.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	21 July 2022
ISSN:	1534-4320
Identifiers:	Number Type 10.1109/tnsre.2022.3192988 DOI 1573725 Other
Rights:	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	25 Jul 2022 15:22
Last Modified:	25 Jul 2022 15:22
URI:	https://irep.ntu.ac.uk/id/eprint/46710