EEG data augmentation: towards class imbalance problem in sleep staging tasks

Abstract

Objective. Automatic sleep staging models suffer from an inherent class imbalance problem (CIP), which hinders the classifiers from achieving a better performance. To address this issue, we systematically studied sleep electroencephalogram data augmentation (DA) approaches. Furthermore, we modified and transferred novel DA approaches from related research fields, yielding new efficient ways to enhance sleep datasets. Approach. This study covers five DA methods, including repeating minority classes, morphological change, signal segmentation and recombination, dataset-to-dataset transfer, as well as generative adversarial network (GAN). We evaluated these mentioned DA methods by a sleep staging model on two datasets, the Montreal archive of sleep studies (MASS) and Sleep-EDF. We used a classification model with a typical convolutional neural network architecture to evaluate the effectiveness of the mentioned DA approaches. We also conducted a comprehensive analysis of these methods. Main results. The classification results showed that DA methods, especially DA by GAN, significantly improved the total classification performance in comparison with the baseline. The improvement of accuracy, F1 score and Cohen Kappa coefficient range from 0.90% to 3.79%, 0.73% to 3.48%, 2.61% to 5.43% on MASS and 1.36% to 4.79%, 1.47% to 4.23%, 2.22% to 4.04% on Sleep-EDF, respectively. DA methods improved the classification performance in most cases, whereas the performance of class N1 showed a subtle degradation in the F1 scores. Significance. Overall, our study proved that DA approaches are efficient in alleviating CIP lying in sleep staging tasks. Meanwhile, this study provided avenues for further improving the sleep staging accuracy using DA methods.

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

“…Sleep datasets suffer from class imbalance problems (CIPs). Several studies have attempted to address CIPs by oversampling minority class samples ( Supratak et al, 2017 ; Fan et al, 2020 ). Such approaches can alleviate weight bias in the networks but fail to produce new patterns to improve the performance of trained models further.…”

Adaptive Margin Aware Complement-Cross Entropy Loss for Improving Class Imbalance in Multi-View Sleep Staging Based on EEG Signals

“…The third class improves the class imbalance problem by augmenting the minority class data, e.g., Sun et al [24] proposed augmenting the minority class by geometric transformations such as flips and translations, F. Lotte [25] proposed amplification of the minority class samples using segmentation and recombination of the original signal, J. Fan, et al [26] generated the minority class of samples by using adversarial generative networks (GANs). However, sleep EEG is time-series data, and such an expansion may change the original time-dependent information of the signal.…”

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

“…Zhao X. et al ( 2020 ) also effectively acquired artificial ictal EEG samples with a discrete cosine transform (DCT)-based spectral transformation. Finally, Fan et al ( 2020 ) and Supratak and Guo ( 2020 ) performed the temporal segmentation and recombination-based DA technique to increase the training data for the sleep stage classification.…”

“…Truong et al ( 2019a , b ) applied DA to STFT transforms of epileptic EEG signals using DCGAN. Finally, Fan et al ( 2020 ) performed the DA using DCGAN to tackle a class imbalance problem in sleep staging tasks and demonstrated the validity of GAN-based DA.…”