结合域对抗训练与CNN-BiLSTM网络的基于EEG的患者间癫痫发作检测
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network
AI总结：
针对癫痫EEG检测跨患者泛化差的问题，提出结合域对抗训练与CNN-BiLSTM的检测框架，通过域对抗提取患者不变特征、BiLSTM建模时间依赖，在20名局灶性癫痫患者数据上实现优于非对抗方法的跨患者检测性能。
摘要：
由于不同患者的脑电图（EEG）模式存在显著个体差异，从EEG中自动检测癫痫发作仍具挑战性。现有研究采用患者特异性方法可实现高精度，但难以推广到新患者。为解决该问题，我们提出一种结合域对抗训练、卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM）的检测框架。首先，CNN通过域对抗训练提取患者不变的局部特征，该训练在优化发作检测精度的同时最小化患者特异性特征。随后，BiLSTM捕捉所提取特征的时间依赖关系，以建模发作演化模式。使用20名局灶性癫痫患者的EEG记录进行评估，结果表明该方法性能优于非对抗方法，在不同患者间均实现高检测精度。对抗训练与时间建模的结合可实现鲁棒的跨患者发作检测。
英文摘要：
Automated epileptic seizure detection from electroencephalogram (EEG) remains challenging due to significant individual differences in EEG patterns across patients. While existing studies achieve high accuracy with patient-specific approaches, they face difficulties in generalizing to new patients. To address this, we propose a detection framework combining domain adversarial training with a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). First, the CNN extracts local patient-invariant features through domain adversarial training, which optimizes seizure detection accuracy while minimizing patient-specific characteristics. Then, the BiLSTM captures temporal dependencies in the extracted features to model seizure evolution patterns. Evaluation using EEG recordings from 20 patients with focal epilepsy demonstrated superior performance over non-adversarial methods, achieving high detection accuracy across different patients. The integration of adversarial training with temporal modeling enables robust cross-patient seizure detection.
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。
