Wagh Neeraj, Duque-Lopez Andrea, Joseph Boney, Berry Brent, Jehi Lara, Crepeau Daniel, Barnard Leland, Gogineni Venkatsampath, Brinkmann Benjamin H, Jones David T, Worrell Gregory, Varatharajah Yogatheesan
Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA.
Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA.
Sci Rep. 2025 Jul 11;15(1):25147. doi: 10.1038/s41598-025-08871-w.
Normal routine electroencephalograms (EEGs) can cause delays in the diagnosis and treatment of epilepsy, especially in drug-resistant patients and those without structural abnormalities. There is a need for alternative quantitative approaches that can inform clinical decisions when traditional visual EEG review is inconclusive. We leverage a large population EEG database (N = 13,652 recordings, 12,134 unique patients) and an independent cohort of patients with focal epilepsy (N = 121) to investigate whether normal EEG segments could support the diagnosis of focal epilepsy. We decomposed expertly graded normal EEGs (N = 6,242) using unsupervised tensor decomposition to extract the dominant spatio-spectral patterns present in a clinical population. We then, using the independent cohort of patients with focal epilepsy, evaluated whether pattern loadings of normal interictal EEG segments could classify focal epilepsy, the epileptogenic lobe, presence of lesions, and drug response. We obtained six physiological patterns of EEG spectral power and connectivity with distinct spatio-spectral signatures. Both pattern types together effectively differentiated patients with focal epilepsy from non-epileptic controls (mean AUC 0.78) but failed to classify the epileptogenic lobe. Spectral power-based patterns best classified drug-resistant epilepsy (mean AUC 0.73) and lesional epilepsy (mean AUC 0.67), albeit with high variability across patients. Our findings support that visibly normal patient EEGs contain subtle quantitative differences of clinical relevance. Further development may yield normal EEG-based computational biomarkers that can augment traditional EEG review and epilepsy care.
常规脑电图（EEG）检查可能会导致癫痫诊断和治疗的延迟，尤其是对于耐药患者和无结构异常的患者。当传统的脑电图视觉检查结果不明确时，需要有替代的定量方法来为临床决策提供依据。我们利用一个大型人群脑电图数据库（N = 13,652份记录，12,134名独特患者）以及一组独立的局灶性癫痫患者队列（N = 121），来研究正常脑电图段是否能支持局灶性癫痫的诊断。我们使用无监督张量分解对专家分级的正常脑电图（N = 6,242）进行分解，以提取临床人群中存在的主要时空谱模式。然后，我们利用独立的局灶性癫痫患者队列，评估正常发作间期脑电图段的模式负荷是否能对局灶性癫痫、致痫叶、病变的存在以及药物反应进行分类。我们获得了六种具有不同时空谱特征的脑电图谱功率和连通性的生理模式。这两种模式类型共同有效地将局灶性癫痫患者与非癫痫对照者区分开来（平均AUC为0.78），但未能对致痫叶进行分类。基于谱功率的模式对耐药性癫痫（平均AUC为0.73）和有病变的癫痫（平均AUC为0.67）的分类效果最佳，尽管患者之间存在很大差异。我们的研究结果支持，表面上正常的患者脑电图包含具有临床相关性的细微定量差异。进一步的发展可能会产生基于正常脑电图的计算生物标志物，从而增强传统的脑电图检查和癫痫治疗。
