Skip to main content

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Phan, Huy, Andreotti, Fernando, Cooray, Navin, Chén, Oliver Y., De Vos, Maarten (2019) SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27 (3). pp. 400-410. ISSN 1534-4320. E-ISSN 1558-0210. (doi:10.1109/TNSRE.2019.2896659)

PDF - Author's Accepted Manuscript
Download (1MB) Preview
[img]
Preview
Official URL
http://doi.org/10.1109/TNSRE.2019.2896659

Abstract

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet. At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modelling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.

Item Type: Article
DOI/Identification number: 10.1109/TNSRE.2019.2896659
Uncontrolled keywords: automatic sleep staging, hierarchical recurrent neural networks, end-to-end, sequence-to-sequence
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Huy Phan
Date Deposited: 02 Feb 2019 02:14 UTC
Last Modified: 15 Jan 2020 09:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72120 (The current URI for this page, for reference purposes)
Phan, Huy: https://orcid.org/0000-0003-4096-785X
  • Depositors only (login required):

Downloads

Downloads per month over past year