Laura Krumm, Dominik D. Kranz, Mustafa Halimeh, Alexander Nelde, Edilberto Amorim, Sahar Zafar, Jin Jing, Robert J. Thomas, M. Brandon Westover, Christian Meisel
{"title":"迈向脑电图的通用地图：用于脑电图分类、聚类和预测的语义、低维流形。","authors":"Laura Krumm, Dominik D. Kranz, Mustafa Halimeh, Alexander Nelde, Edilberto Amorim, Sahar Zafar, Jin Jing, Robert J. Thomas, M. Brandon Westover, Christian Meisel","doi":"10.1002/ana.27260","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>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</p>\n </section>\n </div>","PeriodicalId":127,"journal":{"name":"Annals of Neurology","volume":"98 2","pages":"357-368"},"PeriodicalIF":7.7000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ana.27260","citationCount":"0","resultStr":"{\"title\":\"Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication\",\"authors\":\"Laura Krumm, Dominik D. Kranz, Mustafa Halimeh, Alexander Nelde, Edilberto Amorim, Sahar Zafar, Jin Jing, Robert J. Thomas, M. Brandon Westover, Christian Meisel\",\"doi\":\"10.1002/ana.27260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Interpretation</h3>\\n \\n <p>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. 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Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication
Objective
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.
Methods
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.
Results
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.
Interpretation
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
期刊介绍：
Annals of Neurology publishes original articles with potential for high impact in understanding the pathogenesis, clinical and laboratory features, diagnosis, treatment, outcomes and science underlying diseases of the human nervous system. Articles should ideally be of broad interest to the academic neurological community rather than solely to subspecialists in a particular field. Studies involving experimental model system, including those in cell and organ cultures and animals, of direct translational relevance to the understanding of neurological disease are also encouraged.
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