Volume 12 Number 2 (Jun. 2020)
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IJCEE 2020 Vol.12(2): 58-71 ISSN: 1793-8163
DOI: 10.17706/IJCEE.2020.12.2.58-71

Multi-class Unbalanced Data Classification for Sleep Staging

Dan Li, Wu Huang, Guobiao Xu, Tao Zhang, Zhonghui Jiang, Xiao Wei
Abstract—Unbalanced data classification is a research focus for many applications, including financial fraud detection, network intrusion detection and cancer classification. However, unbalanced data classification is rarely investigated in the field of EEG-based sleep staging. Herein, considering the idea that old methods can be exploited in new applications, we propose a practical framework aiming to classify sleep stages with unbalanced data. In this framework, the data are balanced by using a SMOTE algorithm, in which the mean sample number is used for data expansion and the nearest neighbour number is set according to the G-mean values. Subsequently, the features are extracted and selected based on the balanced dataset. The effectiveness of the proposed framework is validated by testing eight sets of Sleep-EDF EEG data in the MIT-BIH physiological information database. From the results, the proposed framework can be used to not only improve the F-score value of the minority class but also to improve the G-mean value and the AUC value of the whole data set, which might benefit sleep studies and disorder diagnoses.

Index Terms—Multi-class unbalanced data, SMOTE algorithm, feature selection, sleep staging, SVM.

Dan Li, Tao Zhang, Zhonghui Jiang, and Xiao Wei are with Chengdu Techman Software Co., Ltd, Chengdu, China. Wu Huang is with Sichuan University, Chengdu, China. Guobiao Xu is with China Civil Aviation Flight University, Chengdu, China.

Cite:Dan Li, Wu Huang, Guobiao Xu, Tao Zhang, Zhonghui Jiang, Xiao Wei, "Multi-class Unbalanced Data Classification for Sleep Staging," International Journal of Computer and Electrical Engineering vol. 12, no. 2, pp. 58-71, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 1793-8163 (Print)
Abbreviated Title: Int. J. Comput. Electr. Eng.
Frequency: Quarterly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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