Automatic seizure detection in long-term scalp EEG using an adaptive thresholding technique: a validation study for clinical routine.

ABSTRACT

OBJECTIVE: In a previous study we proposed a robust method for automatic seizure detection in scalp EEG recordings. The goal of the current study was to validate an improved algorithm in a much larger group of patients in order to show its general applicability in clinical routine. METHODS: For the detection of seizures we developed an algorithm based on Short Time Fourier Transform, calculating the integrated power in the frequency band 2.5-12 Hz for a multi-channel seizure detection montage referenced against the average of Fz-Cz-Pz. For identification of seizures an adaptive thresholding technique was applied. Complete data sets of each patient were used for analyses for a fixed set of parameters. RESULTS: 159 patients (117 temporal-lobe epilepsies (TLE), 35 extra-temporal lobe epilepsies (ETLE), 7 other) were included with a total of 25,278 h of EEG data, 794 seizures were analyzed. The sensitivity was 87.3% and number of false detections per hour (FpH) was 0.22/h. The sensitivity for TLE patients was 89.9% and FpH=0.19/h; for ETLE patients sensitivity was 77.4% and FpH=0.25/h. CONCLUSIONS: The seizure detection algorithm provided high values for sensitivity and selectivity for unselected large EEG data sets without a priori assumptions of seizure patterns. SIGNIFICANCE: The algorithm is a valuable tool for fast and effective screening of long-term scalp EEG recordings.