On the effect of reduced sampling rate and bitwidth on seizure detection

Ambulatory EEG requires signal processing algorithms which are efficient in terms of how much power they use in their computation, while at the same time providing accurate decision-making capabilities. Two methods of achieving this are to downsample the EEG and to perform bitwidth reduction on the data. The effect of performing both of these techniques to varying extents is investigated in this paper. Frequency, time and entropy based features are extracted from the data and used to train a support vector machine (SVM) classification system. The effect of changing the overlap between successive epochs is also investigated.