Skip to main content

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Phan, Huy, Andreotti, Fernando, Cooray, Navin, Chén, Oliver Y., De Vos, Maarten (2018) Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification. IEEE Transactions on Biomedical Engineering, . ISSN 0018-9294. (doi:10.1109/TBME.2018.2872652) (KAR id:72662)

PDF Publisher pdf
Language: English

Download (480kB) Preview
[thumbnail of 08502139.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
PDF Author's Accepted Manuscript
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of Phan2018e.pdf]
Official URL


Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs' labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

Item Type: Article
DOI/Identification number: 10.1109/TBME.2018.2872652
Uncontrolled keywords: sleep stage classification, joint classification and prediction, convolutional neural network, multi-task
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Huy Phan
Date Deposited: 22 Feb 2019 10:45 UTC
Last Modified: 16 Feb 2021 14:02 UTC
Resource URI: (The current URI for this page, for reference purposes)
Phan, Huy:
  • Depositors only (login required):


Downloads per month over past year