Evaluation of automatic feature detection algorithms in EEG: Application to interburst intervals
A software to improve algorithms for feature detection in neonatal EEG is proposed. The clustering quality of feature detectors with EEG rating is quantified. The method is tested on a modular definition of interburst intervals detectors. The power supply filter can be removed with little effect on the detection quality. Detectors are robust compared with the standard deviation thresholding of the EEG.
eeg interburst intervals clustering signal analysis cluster validity index