比较三种脑电图表示法，发现频域数据能实现超97%癫痫发作检测准确率
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy Detection
- 对比时域、频域和时频域的脑电图表示，评估其对深度模型的影响
- 频域数据在癫痫检测中达到超过97%的性能，显著优于其他表示方式
- 为临床级自动癫痫预警系统提供可复现的数据处理与模型设计依据
全球约有5000万癫痫患者，其特征为异常脑电活动，治疗仍具挑战性。癫痫诊断严重依赖脑电图（EEG）数据，需专家手动分析发作前、发作中、发作后及发作间期的痫样波形。然而，人工分析存在专家间差异，亟需自动化解决方案。尽管已有研究探索预处理技术和机器学习方法用于癫痫发作检测，但尚缺乏对脑电图数据表示形式（时域、频域或时频域）如何影响深度学习模型预测性能的理解。本文系统比较了在三种数据表示下训练的卷积与循环神经网络的性能。通过统计检验，识别出最优数据表示与模型架构。结果表明，频域数据可实现超过97%的检测指标，为构建更精准可靠的癫痫发作检测系统奠定坚实基础。
原文摘要 · Abstract (English)
Epilepsy, affecting approximately 50 million people globally, is characterized by abnormal brain activity and remains challenging to treat. The diagnosis of epilepsy relies heavily on electroencephalogram (EEG) data, where specialists manually analyze epileptiform patterns across pre-ictal, ictal, post-ictal, and interictal periods. However, the manual analysis of EEG signals is prone to variability between experts, emphasizing the need for automated solutions. Although previous studies have explored preprocessing techniques and machine learning approaches for seizure detection, there is a gap in understanding how the representation of EEG data (time, frequency, or time-frequency domains) impacts the predictive performance of deep learning models. This work addresses this gap by systematically comparing deep neural networks trained on EEG data in these three domains. Through the use of statistical tests, we identify the optimal data representation and model architecture for epileptic seizure detection. The results demonstrate that frequency-domain data achieves detection metrics exceeding 97\%, providing a robust foundation for more accurate and reliable seizure detection systems.
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