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

Jiahao Fan1,2, Chenglu Sun1, Chen Chen1

  • 1Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.

Journal of Neural Engineering
|October 15, 2020
PubMed
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This summary is machine-generated.

Data augmentation (DA) methods effectively address class imbalance in automatic sleep staging. Generative adversarial networks (GANs) show significant improvements in classification performance for sleep electroencephalogram data.

Area of Science:

  • Medical Informatics
  • Machine Learning
  • Signal Processing

Background:

  • Automatic sleep staging models face challenges due to class imbalance, limiting classifier performance.
  • Sleep electroencephalogram (EEG) data analysis is crucial for diagnosing sleep disorders.

Purpose of the Study:

  • To systematically investigate data augmentation (DA) approaches for improving sleep staging models.
  • To adapt and develop novel DA techniques from related fields to enhance sleep datasets.

Main Methods:

  • Evaluated five DA methods: repeating minority classes, morphological change, signal segmentation/recombination, dataset-to-dataset transfer, and generative adversarial networks (GANs).
  • Utilized a convolutional neural network (CNN) classifier on the Montreal archive of sleep studies (MASS) and Sleep-EDF datasets.

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  • Conducted a comprehensive analysis of DA method effectiveness.

    • Main Results:

    • DA methods, particularly GANs, significantly enhanced overall classification performance compared to baseline models.
    • Accuracy, F1 score, and Cohen Kappa coefficient improvements were observed across both datasets.
    • Minor degradation in F1 scores for the N1 sleep stage was noted.

    Conclusions:

    • DA approaches are effective in mitigating class imbalance problems in sleep staging.
    • This study offers pathways for enhancing sleep staging accuracy through advanced DA techniques.