用多域特征融合提升无癫痫波脑电的诊断准确率
Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
- 融合时序、频谱、小波与连接性特征，构建集成学习模型
- 在无癫痫波情况下，刺激诱发脑电的分类准确率达97.8% AUC
- 特别适合临床缺乏典型放电信号的癫痫辅助诊断
当常规脑电图缺乏间期癫痫样放电（IEDs）时，癫痫诊断极具挑战。闪光刺激（IPS）和过度换气（HV）可提高诊断率，但其解读主观性强。本文提出一种可复现的分析流程，利用机器学习方法提取时序、频谱、小波及连通性等多域特征，并采用堆叠集成模型融合互补特征。在TUH癫痫语料库和临床埃拉斯谟医疗中心（EMC）队列上，通过留一被试交叉验证评估性能，包括对TUH数据集的无IED分析。在TUH数据集上，集成模型在无IED静息态脑电中达到最高97.8% AUC / 93.1% BAC，无IED IPS下达94.1% AUC / 86.8% BAC；在EMC队列中，IPS表现最优（79.4% AUC / 73.9% BAC），而HV效果因按反应性分层而提升。结果表明，刺激诱发活动（尤其IPS）蕴含对无IED癫痫分类具有判别意义的信息，且多域集成显著增强模型鲁棒性。
原文摘要 · Abstract (English)
Diagnosing epilepsy is challenging when routine EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) and hyperventilation (HV) can increase diagnostic yield, but their interpretation is subjective. We propose a reproducible pipeline that classifies EEG recordings acquired during stimulation procedures, using machine-learning features spanning temporal, spectral, wavelet, and connectivity domains, and a stacked ensemble to combine complementary feature sets. Performance is evaluated with leave-one-subject-out (LOSO) cross-validation on the TUH Epilepsy Corpus and a clinical Erasmus MC (EMC) cohort, including IED-free analyses on TUH. On TUH, ensembles achieve up to 97.8\% AUC / 93.1\% BAC on IED-free resting-state EEG and 94.1\% AUC / 86.8\% BAC on IED-free IPS. On EMC, IPS provides the strongest discrimination (79.4\% AUC / 73.9\% BAC), while HV performance benefits from stratifying subjects by responsiveness. These results indicate that stimulation-evoked activity, particularly IPS, contains meaningful discriminative information for IED-free epilepsy classification and that multi-domain ensembling improves robustness.
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