EEG-DIF：基于生成扩散模型的多通道脑电信号预测实现癫痫发作早期预警
EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting
AI总结：
本研究提出EEG-DIF，将多通道脑电预测转化为图像补全任务，利用生成扩散模型建模时空相关性，实现癫痫发作早期预警，预警准确率达0.89。
摘要：
多通道脑电（EEG）信号常用于癫痫等疾病的诊断与评估。目前，已有多种基于深度学习的脑电诊断算法被开发出来，但大多数研究仅关注对当前信号数据的诊断与分类，未考虑对未来趋势的预测以实现早期预警。此外，由于多通道脑电本质上可视为大脑不同位置探测器接收到的时空信号数据，如何构建脑电信号的时空信息表示以促进多通道脑电的未来趋势预测成为一个重要问题。本研究提出一种基于生成扩散模型的多信号预测算法（EEG-DIF），该算法将多信号预测任务转化为图像补全任务，从而能够对多通道脑电信号的时空相关性及未来演化模式进行全面表示与学习。我们使用公开的癫痫脑电数据集构建并验证了EEG-DIF。结果表明，该方法能够同时准确预测多通道脑电信号的未来趋势。此外，基于所生成脑电数据的癫痫发作早期预警准确率达到0.89。总体而言，EEG-DIF为多通道脑电信号的表征提供了一种新方法，并为癫痫发作提供了一种创新的早期预警算法，有助于优化和增强临床诊断流程。代码可在 https://github.com/JZK00/EEG-DIF 获取。
英文摘要：
Multi-channel EEG signals are commonly used for the diagnosis and assessment of diseases such as epilepsy. Currently, various EEG diagnostic algorithms based on deep learning have been developed. However, most research efforts focus solely on diagnosing and classifying current signal data but do not consider the prediction of future trends for early warning. Additionally, since multi-channel EEG can be essentially regarded as the spatio-temporal signal data received by detectors at different locations in the brain, how to construct spatio-temporal information representations of EEG signals to facilitate future trend prediction for multi-channel EEG becomes an important problem. This study proposes a multi-signal prediction algorithm based on generative diffusion models (EEG-DIF), which transforms the multi-signal forecasting task into an image completion task, allowing for comprehensive representation and learning of the spatio-temporal correlations and future developmental patterns of multi-channel EEG signals. Here, we employ a publicly available epilepsy EEG dataset to construct and validate the EEG-DIF. The results demonstrate that our method can accurately predict future trends for multi-channel EEG signals simultaneously. Furthermore, the early warning accuracy for epilepsy seizures based on the generated EEG data reaches 0.89. In general, EEG-DIF provides a novel approach for characterizing multi-channel EEG signals and an innovative early warning algorithm for epilepsy seizures, aiding in optimizing and enhancing the clinical diagnosis process. The code is available at https://github.com/JZK00/EEG-DIF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
