用轻量模型和自适应选通道，8通道实现94.8%癫痫预测准确率
SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network
- 先机器学习选最优8通道，再用轻量卷积-Mamba网络预测
- 仅21.2K参数下达94.8%准确率、95.5%敏感度，优于大模型
- 适合移动端部署，跨患者数据集验证有效
癫痫发作由脑电异常引发，其不可预测性易导致事故，亟需长期预测。尽管可通过脑电图（EEG）信号预测发作，现有方法常需大量电极通道或大模型，限制移动应用。本文提出SlimSeiz框架，结合自适应通道选择与轻量神经网络。第一阶段使用机器学习算法筛选最优通道组合；第二阶段采用基于卷积与Mamba的轻量网络进行预测。在儿童医院波士顿-麻省理工（CHB-MIT）EEG数据集上，通道数从22降至8，仍达到94.8%准确率、95.5%敏感度、94.0%特异性，模型仅21.2K参数，性能匹配或超越更大模型。在复旦大学附属上海仁济医院新采集的SRH-LEI数据集上也验证了跨患者有效性。代码与数据集已开源。
原文摘要 · Abstract (English)
Epileptic seizures cause abnormal brain activity, and their unpredictability can lead to accidents, underscoring the need for long-term seizure prediction. Although seizures can be predicted by analyzing electroencephalogram (EEG) signals, existing methods often require too many electrode channels or larger models, limiting mobile usability. This paper introduces a SlimSeiz framework that utilizes adaptive channel selection with a lightweight neural network model. SlimSeiz operates in two states: the first stage selects the optimal channel set for seizure prediction using machine learning algorithms, and the second stage employs a lightweight neural network based on convolution and Mamba for prediction. On the Children's Hospital Boston-MIT (CHB-MIT) EEG dataset, SlimSeiz can reduce channels from 22 to 8 while achieving a satisfactory result of 94.8% accuracy, 95.5% sensitivity, and 94.0% specificity with only 21.2K model parameters, matching or outperforming larger models' performance. We also validate SlimSeiz on a new EEG dataset, SRH-LEI, collected from Shanghai Renji Hospital, demonstrating its effectiveness across different patients. The code and SRH-LEI dataset are available at https://github.com/guoruilu/SlimSeiz.
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