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EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms

NeurIPS 2017 · Graph learning › Knowledge and temporal graphs · approach: Continuous-time dynamic graph learning

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use spectral clustering instead of k-means for abnormal-event mapping (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsAUC: 0.72 -> 0.68Hurt
use hierarchical clustering instead of k-means (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsAUC: 0.72 -> 0.69Hurt
use SVM instead of k-means (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsAUC: 0.72 -> 0.71Hurt
use GLM instead of k-means (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsAUC: 0.72 -> 0.69Hurt
replace min-cut with MCMC random sampling (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsAUC: 0.72 -> 0.62Hurt
replace min-cut with MCMC sampling per prior (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsAUC: 0.72 -> 0.69Hurt
replace min-cut with MCMC random sampling (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsF1-score: 0.46 -> 0.32Hurt
replace min-cut with MCMC sampling per prior (ablation)FG/kmeans/min-cut · Interictal SOZ localization, 29 patientsF1-score: 0.46 -> 0.46No clear effect
add spatial and temporal factors versus summation baselineObservation-only summation · Interictal SOZ localization, 29 patientsAUC: 0.67 -> 0.72Helped
add spatial and temporal factors versus clustering baselineObservation-only clustering · Interictal SOZ localization, 29 patientsAUC: 0.65 -> 0.72Helped
add spatial and temporal factors versus clustering baselineObservation-only clustering · Interictal SOZ localization, 29 patientsspecificity: 0.72 -> 0.61Mixed

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