Early Detection of Human Epileptic Seizures Based on Intracortical Microelectrode Array Signals

OBJECTIVE: We examine, for the first time, the use of intracortical microelectrode array (MEA) signals for early detection of human epileptic seizures. METHODS: 96-channel-MEA recordings were obtained during neuro-monitoring preceding resective surgery in five participants. The participant-specific seizure-detection framework consisted of: first, feature extraction from local field potentials (LFPs) and multiunit activity (MUA); second, nonlinear cost-sensitive support vector machine (SVM) classification of ictal and interictal states based on LFP, MUA, and combined LFP-MUA (a SVM was trained for each participant separately); and third, Kalman filter postprocessing of SVM scoring functions. Performance was assessed on data including 17 seizures and 39.0 h interictal and preictal recordings. RESULTS: The use of combined LFP-MUA features resulted in 100% sensitivity with short detection latency (average: 2.7 s; median: 2.5 s) and five false alarms (0.13/h). The average detection performance based on the area under the receiver operating characteristic corresponded to 0.97. Importantly, technically false alarms were related to epileptiform activity, subclinical seizures, and recording artifacts. Extreme gradient boosting classifiers ranked features based on LFP spectral coherence or MUA count among the top features for seizures characterized by spike-wave complexes, whereas features related to LFP power spectra were ranked higher for seizures characterized by sustained gamma LFP oscillations. CONCLUSION: The combination of intracortical LFP and MUA signals may allow reliable detection of human epileptic seizures by improving latency and false alarm rate. SIGNIFICANCE: Intracortical MEAs provide promising signals for closed-loop seizure-control systems based on seizure early-detection in people with pharmacologically resistant epilepsies.