Seizure state detection of temporal lobe seizures by autoregressive spectral analysis of scalp EEG

Abstract

Objective: To investigate a novel application of autoregression (AR) spectral techniques for seizure detection from scalp EEG.

Methods: EEGs were recorded from twelve patients with left temporal lobe epilepsy. The Burg maximum entropy AR method was applied to the signals from four electrodes near the epileptic focus for each patient, and the AR spectra were parameterized based on scalp EEG features described by a neurologist, thus mimicking clinical seizure identification. The parameters measured spectral peak power, sharpness, and location in a delta/low theta frequency range. An optimized nonlinear seizure detection index, which accounted for spatial and temporal persistence of behavior, was then calculated.

Results: Performance was optimized using recordings from two patients (315h, 18 seizures). For the remaining 10 patients (1624h, 83 seizures) results are presented as a Receiver Operating Characteristic graph, yielding an overall event-based true positive rate of 91.57% and epoch-based false positive rate of 3.97%.

Conclusions: Performance of the AR seizure identification method is comparable to other approaches. Techniques such as artifact removal are expected to improve performance.

Significance: There is a real potential for this seizure detection method to be of practical clinical use in long-term monitoring.