对比多种深度学习模型，提升脑电图中异常活动的自动识别准确率。
Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG
- 融合原始信号与小波变换谱图，多模态输入增强特征表达
- 多阶段训练策略使TinyViT和EfficientNet在分类任务中表现最优
- 强调训练方法比模型结构更关键，适合临床AI系统开发者参考
有害脑活动（如癫痫发作、周期性放电）的分类在神经重症监护中至关重要，有助于及时诊断与干预。脑电图（EEG）提供了无创监测脑活动的方法，但人工解读耗时且依赖专家经验。本研究对比了卷积神经网络（CNN）、视觉变换器（ViTs）和EEGNet等深度学习架构，应用于原始EEG数据及通过连续小波变换（CWT）生成的时间-频率表示。评估了高分辨率频谱图与波形数据的多模态表示，并引入多阶段训练策略以提升模型鲁棒性。结果表明，训练策略、数据预处理与增强技术对模型性能的影响与架构选择相当，其中多阶段TinyViT与EfficientNet表现最佳。研究强调了稳健训练方案在实现高效、准确的EEG分类中的关键作用，为人工智能在临床实践中的部署提供了重要参考。
原文摘要 · Abstract (English)
The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-consuming and rely heavily on expert judgment. This study presents a comparative analysis of deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and EEGNet, applied to the classification of harmful brain activities using both raw EEG data and time-frequency representations generated through Continuous Wavelet Transform (CWT). We evaluate the performance of these models use multimodal data representations, including high-resolution spectrograms and waveform data, and introduce a multi-stage training strategy to improve model robustness. Our results show that training strategies, data preprocessing, and augmentation techniques are as critical to model success as architecture choice, with multi-stage TinyViT and EfficientNet demonstrating superior performance. The findings underscore the importance of robust training regimes in achieving accurate and efficient EEG classification, providing valuable insights for deploying AI models in clinical practice.
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