Multi-class Unbalanced Data Classification for Sleep Staging

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.