Discovering Novel intracranial EEG Biomarkers of Seizure Generating Tissue through Time-Frequency Analysis
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Objective
EEG biomarkers for seizure-generating tissue have historically been identified visually, which lacks objectivity and limits utility of automated approaches. For example, high frequency oscillations and interictal epileptiform discharges were promising markers to improve surgical outcomes for refractory epilepsy, but low specificity has hindered clinical implementation, and automated algorithms have not improved this.
Methods
We developed Intracranial EEG Pattern Identification and Categorization, an automated, data-driven time-frequency framework for EEG biomarker discovery. It detects transient high-power intracranial EEG waveforms (1-500 Hz) and characterizes them using eight features. In seizure-free patients, waveforms occurring predominantly in resected intracranial EEG channels are candidate biomarkers.
Results
In retrospective data from 14 seizure-free post-surgical patients from University of California, Los Angeles, we identified 9 waveform categories strongly associated with resected intracranial EEG channels. These included beta, gamma, and ripple band bursts, sometimes co-occurring with interictal epileptiform discharges; however, many were visually imperceptible in the broadband EEG. Using a support vector machine, we generated a unified classification metric based on these waveforms and tested it on 87 seizure-free subjects from Detroit Medical Center. This metric achieved higher area under the precision-recall curve than six state-of-the-art benchmark algorithms (p<0.001, corrected) and higher positive predictive value than three algorithms (p<0.01, corrected). Retraining the support vector machine on the Detroit dataset with five-fold cross-validation, the metric outperformed all six benchmarks across performance metrics.
Interpretation
Our analysis framework identified novel intracranial EEG biomarkers for seizure-generating tissue, outperforming traditional markers and generalizing across datasets, providing a new avenue for EEG biomarker discovery.
