  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.
