Data Augmentation for Seizure Prediction with Generative Diffusion Model

Objective: Seizure prediction is of great importance to improve the life of patients. The focal point is to distinguish preictal states from interictal ones. With the development of machine learning, seizure prediction methods have achieved significant progress. However, the severe imbalance problem between preictal and interictal data still poses a great challenge, restricting the performance of classifiers. Data augmentation is an intuitive way to solve this problem. Existing data augmentation methods generate samples by overlapping or recombining data. The distribution of generated samples is limited by original data, because such transformations cannot fully explore the feature space and offer new information. As the epileptic EEG representation varies among seizures, these generated samples cannot provide enough diversity to achieve high performance on a new seizure. As a consequence, we propose a novel data augmentation method with diffusion model called DiffEEG. Methods: Diffusion models are a class of generative models that consist of two processes. Specifically, in the diffusion process, the model adds noise to the input EEG sample step by step and converts the noisy sample into output random noise, exploring the distribution of data by minimizing the loss between the output and the noise added. In the denoised process, the model samples the synthetic data by removing the noise gradually, diffusing the data distribution to outward areas and narrowing the distance between different clusters. Results: We compared DiffEEG with existing methods, and integrated them into three representative classifiers. The experiments indicate that DiffEEG could further improve the performance and shows superiority to existing methods. Conclusion: This paper proposes a novel and effective method to solve the imbalanced problem and demonstrates the effectiveness and generality of our method.