A4 Article in conference proceedings
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance (2022)

Xu, Q., Zhou, D., Wang, J., Shen, J., Kettunen, L., & Cong, F. (2022). Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance. In IJCNN 2022 : Proceedings of the 2022 International Joint Conference on Neural Networks. IEEE. Proceedings of International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn55064.2022.9892741

JYU authors or editors

Publication details

All authors or editors: Xu, Qi; Zhou, Dongdong; Wang, Jian; Shen, Jiangrong; Kettunen, Lauri; Cong, Fengyu

Parent publication: IJCNN 2022 : Proceedings of the 2022 International Joint Conference on Neural Networks

Place and date of conference: Padua, Italy, 18.-23.7.2022

eISBN: 978-1-7281-8671-9

Journal or series: Proceedings of International Joint Conference on Neural Networks

ISSN: 2161-4393

eISSN: 2161-4407

Publication year: 2022

Publication date: 18/07/2022

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/ijcnn55064.2022.9892741

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/85542

Abstract

Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolutional neural network (CNN) based model. Experimental results evaluated on Sleep-EDF-V1, Sleep-EDF and CCSHS databases demonstrate that the proposed balancing approaches with specific tensity Gaussian white noise could enhance the overall or stage N1 recognition to some degree, especially the combination of two types of Data augmentation (DA) strategies shows the superiority of overall accuracy improvement.

Keywords: sleep; sleep disorders; modelling (representation); neural networks (information technology); deep learning; databases; classification

Free keywords: training; deep learning; databases; neural networks; white noise; convolutional neural networks; sleep stage classification; class imbalance problem; data augmentation; time-frequency image

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2022

JUFO rating: 1