Krumm Laura, Kranz Dominik D, Halimeh Mustafa, Nelde Alexander, Amorim Edilberto, Zafar Sahar, Jing Jin, Thomas Robert J, Westover M Brandon, Meisel Christian
Computational Neurology, Department of Neurology and Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Bernstein Center for Computational Neuroscience, Berlin, Germany.
Ann Neurol. 2025 Aug;98(2):357-368. doi: 10.1002/ana.27260. Epub 2025 Jun 20.
Prognostication in patients with disorders of consciousness (DOCs) remains challenging because of heterogeneous etiologies, pathophysiologies and, consequently, highly variable electroencephalograms (EEGs). Here, we use EEG patterns that are well-characterizable to create a latent map that positions novel EEGs along a continuum. We asses this map as a generalizable tool to extract prognostically valuable information from long-term EEG, by predicting outcome post-cardiac arrest as a first use case.
Categorizable EEGs across the health-disease continuum (wake [W], sleep [rapid eye movement (REM), non-REM (N1, N2, N3)], ictal-interictal-continuum [lateralized and generalized periodic discharges (LPD, GPD) and lateralized and generalized rhythmic delta activity (LRDA, GRDA)], seizures [SZ], burst suppression [BS]; 20,043 patients, 288,986 EEG segments) are arranged meaningfully in a low-dimensional space via a deep neural network, resulting in a universal map of EEG (UM-EEG). We assess prognostication after cardiac arrest (576 patients, recovery or death) based on long-term EEGs represented as trajectories in this continuous embedding space.
Classification of out-of-sample EEG match state-of-the-art artificial intelligence algorithms while extending it to the currently largest set of classes across the health-disease continuum (mean area under the receiver-operating-characteristic curve [AUROCs] 1-vs-all classification: W, 0.94; REM, 0.92; N1, 0.85; N2, 0.91; N3, 0.98; GRDA, 0.97; LRDA, 0.97; SZ, 0.87; GPD, 0.99; LPD, 0.97; BS, 0.94). UM-EEG enables outcome prediction after cardiac arrest with an AUROC of 0.86 and identifies interpretable factors governing prognosis such as the distance to healthy states over time.
UM-EEG presents a novel and physiologically meaningful representation to characterize brain states along the health-disease continuum. It offers new opportunities for personalized, long-term monitoring and prognostication. ANN NEUROL 2025;98:357-368.
意识障碍（DOCs）患者的预后判断仍然具有挑战性，因为其病因、病理生理各不相同，脑电图（EEG）也高度多变。在此，我们使用特征明确的EEG模式来创建一个潜在图谱，将新的EEG沿着一个连续体进行定位。我们将此图谱作为一种可推广的工具，通过预测心脏骤停后的结局作为首个应用案例，从长期EEG中提取具有预后价值的信息。
通过深度神经网络将健康 - 疾病连续体（清醒[W]、睡眠[快速眼动（REM）、非快速眼动（N1、N2、N3）]、发作期 - 发作间期连续体[局灶性和全身性周期性放电（LPD、GPD）以及局灶性和全身性节律性δ活动（LRDA、GRDA）]、癫痫发作[SZ]、爆发抑制[BS]；20,043例患者，288,986个EEG片段）中可分类的EEG有意义地排列在一个低维空间中，从而得到一个EEG通用图谱（UM - EEG）。我们基于在此连续嵌入空间中表示为轨迹的长期EEG评估心脏骤停后的预后（576例患者，恢复或死亡）。
样本外EEG的分类与最先进的人工智能算法相当，同时将其扩展到健康 - 疾病连续体中目前最大的类别集（受试者操作特征曲线下平均面积[AUROCs] 1对所有分类：W，0.94；REM，0.92；N1，0.85；N2，0.91；N3，0.98；GRDA，0.97；LRDA，0.97；SZ，0.87；GPD，0.99；LPD，0.97；BS，0.94）。UM - EEG能够以0.86的AUROC预测心脏骤停后的结局，并识别出影响预后的可解释因素，如随时间与健康状态的距离。
UM - EEG提供了一种新颖且具有生理意义的表征，用于刻画健康 - 疾病连续体中的脑状态。它为个性化的长期监测和预后判断提供了新机会。《神经病学年鉴》2025年；98：357 - 368。
