基于两阶段通道感知集合Transformer网络的脑电癫痫预测
EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
AI总结：
本文提出两阶段通道感知集合Transformer网络，在CHB-MIT数据集上实现用更少脑电通道进行癫痫预测，通道数从18降至2.8，灵敏度提升至80.1%，并验证了癫痫无关划分的必要性。
摘要：
癫痫是一种慢性、非传染性脑部疾病，突发性癫痫发作会严重影响患者的生活质量和健康。然而，可穿戴癫痫预测设备仍然有限，部分原因是脑电采集设备体积庞大。为缓解这一问题，我们提出了一种新颖的两阶段通道感知集合Transformer网络，该网络能够在更少的脑电通道传感器条件下进行癫痫预测。我们还测试了一种癫痫无关的数据划分方法，该方法可以防止训练数据与测试数据相邻。实验在CHB-MIT数据集上进行，该数据集包含22名患者共88次合并后的癫痫发作。通道选择前的平均灵敏度为76.4%，误报率为每小时0.09次。通道选择后，22名患者中有20名出现了主导通道；平均通道数从18个减少到2.8个；平均灵敏度提升至80.1%，误报率为每小时0.11次。此外，癫痫无关划分的实验结果支持了我们的观点：对于拥有大量脑电记录的患者，应采用更严格的癫痫无关划分方法。
英文摘要：
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.
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。
