dc.contributor.author | Phan, H | en_US |
dc.contributor.author | Andreotti, F | en_US |
dc.contributor.author | Cooray, N | en_US |
dc.contributor.author | Chen, OY | en_US |
dc.contributor.author | De Vos, M | en_US |
dc.date.accessioned | 2020-06-17T10:15:32Z | |
dc.date.available | 2019-01-27 | en_US |
dc.date.issued | 2019-03 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/65048 | |
dc.description.abstract | Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, 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 (source code is available at http://github.com/pquochuy/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 modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling 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 the 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. | en_US |
dc.format.extent | 400 - 410 | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | IEEE Trans Neural Syst Rehabil Eng | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Attention | en_US |
dc.subject | Databases, Factual | en_US |
dc.subject | Electroencephalography | en_US |
dc.subject | Electromyography | en_US |
dc.subject | Electrooculography | en_US |
dc.subject | Humans | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Neural Networks, Computer | en_US |
dc.subject | Polysomnography | en_US |
dc.subject | Reproducibility of Results | en_US |
dc.subject | Sleep Stages | en_US |
dc.subject | Software | en_US |
dc.title | SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. | en_US |
dc.type | Article | |
dc.rights.holder | © 2019 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. | |
dc.identifier.doi | 10.1109/TNSRE.2019.2896659 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/30716040 | en_US |
pubs.issue | 3 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 27 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |