用对抗训练+CNN-BiLSTM实现跨患者癫痫发作精准检测
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network
- 通过对抗训练提取共性特征，减少个体差异影响
- 在20名患者数据上达到高跨患者检测准确率
- 适合需要通用化癫痫监测系统的临床场景
基于脑电图（EEG）的自动癫痫发作检测因患者间显著的个体差异而面临挑战。现有患者特异性方法虽精度高，但难以泛化到新患者。为此，我们提出结合领域对抗训练与卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）的检测框架。首先，CNN通过领域对抗训练提取患者无关的局部特征，优化发作检测准确率的同时最小化患者特异性特征；随后，BiLSTM捕获提取特征中的时序依赖关系，建模发作演化模式。在20名局灶性癫痫患者的脑电记录上评估表明，该方法优于非对抗性方法，在不同患者间均实现高检测准确率。对抗训练与时序建模的融合实现了稳健的跨患者癫痫发作检测。
原文摘要 · Abstract (English)
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.
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