用少通道脑电图预测癫痫发作，准确率超80%
EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
- 分两阶段的通道感知变换器，仅需少量脑电传感器
- 通道数从18减至2.8，平均准确率达80.1%，误报率0.11次/小时
- 适合需要小型化可穿戴设备的癫痫患者研究
癫痫是一种慢性非传染性脑部疾病，突发性发作严重影响患者生活质量与健康。然而，当前可穿戴预警设备仍受限于脑电采集装置体积庞大。为此，我们提出一种新型两阶段通道感知的Set Transformer网络，可在减少脑电通道数量的前提下实现癫痫发作预测。同时采用独立于发作事件的数据划分方法，避免训练与测试数据相邻。实验基于包含22名患者、88次合并发作的CHB-MIT数据集进行。通道选择前平均敏感度为76.4%，误报率为0.09次/小时；通道选择后，20名患者出现主导通道，平均通道数降至2.8（原为18），平均敏感度提升至80.1%，误报率上升至0.11次/小时。此外，独立于发作的划分方法验证了对记录丰富的患者应采用更严格的分割策略。
原文摘要 · Abstract (English)
Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent the adjacency of training and test data. Experiments were performed on the CHB-MIT dataset which includes 22 patients with 88 merged seizures. The mean sensitivity before channel selection was 76.4% with a false predicting rate (FPR) of 0.09/hour. After channel selection, dominant channels emerged in 20 out of 22 patients; the average number of channels was reduced to 2.8 from 18; and the mean sensitivity rose to 80.1% with an FPR of 0.11/hour. Furthermore, experimental results on the seizure-independent division supported our assertion that a more rigorous seizure-independent division should be used for patients with abundant EEG recordings.
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