Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity

Young Tak Kim, Hayom Kim, Mingyeong So, Jooheon Kong, Keun Tae Kim, Je Hyeong Hong, Yunsik Son, Jason K. Sa, Synho Do, Jae Ho Han, Jung Bin Kim

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.

    Original languageEnglish
    Article number120749
    JournalNeuroImage
    Volume297
    DOIs
    Publication statusPublished - 2024 Aug 15

    Bibliographical note

    Publisher Copyright:
    © 2024 The Authors

    Keywords

    • EEG coherence network
    • Explainable artificial intelligence
    • Functional connectivity
    • Graph theory
    • Loss of consciousness

    ASJC Scopus subject areas

    • Neurology
    • Cognitive Neuroscience

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