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

Sleep data are typically characterized by class imbalance, which can cause the model to be overly biased toward frequent classes, resulting in low accuracy of minority class classification. However, the minority class of sleep staging has important value in diagnosing certain disorders, such as an N1 Stage that is too short indicating possible hypersomnia or narcolepsy. To address this problem, we propose a multi-view CNN model based on adaptive margin-aware loss. A novel margin-aware factor that considers the relative sample sizes of both frequent and minority classes can improve the overfitting of minority classes by increasing the regularization strength of minority classes without changing the sample size to maximize the decision margins of minority classes. On this basis, we propose margin-aware cross-entropy and margin-aware complement entropy loss, respectively. Margin-aware complement entropy can be achieved to increase the regularization for minority classes while neutralizing errors for minority classes, thus improving the classification accuracy for minority classes. Finally, the synergy of margin-aware complement entropy and cross-entropy is realized in an adaptive way to improve the sleep staging classification accuracy. We tested on three sleep datasets and compared them with the state-of-the-art, and the results demonstrate that our proposed algorithm not only improves the accuracy of sleep staging in general, but also improves the minority classes to a greater extent.

Automated summary

Sleep data often have class imbalance, affecting the accuracy of minority class classification. To address this issue, a multi-view CNN model based on adaptive margin-aware loss is proposed to improve the overfitting of minority classes.