Automatic detection of epileptiform activity by single-level wavelet analysis

We describe a new strategy to automatically identify epileptiform activity in EEG. Our scheme is based upon detecting epileptic spikes, via multiresolution analysis, a relatively new tool in signal processing, which allows for dramatic improvements in the efficiency of basic wavelet analysis. We perform a single-level analysis, which is fast and delivers satisfactory results, provided a wise strategy is adopted. Key points are: the identification of suitable wavelets, in order to gain high computational efficiency; the recognition of a proper resolution level; the computation of an appropriate dynamic threshold, in order to pick out the pathological events. Using a suitable wavelet as the model of a threshold-event proved to be a good choice for devising an algorithm which efficiently performs automatic analysis at high-sensitivity levels. The proposed algorithm was implemented into a C++ multiplatform code having an user-friendly interface, which runs on general-purpose PCs. Results obtained on a set of test tracings, show that the sensitivity of the automatic analysis can be as high as 96%, while less than 5% of the overall recording time is marked. The computational complexity of our algorithm is O (N). Its highly efficient implementation allows for the analysis of up to 310 s of 8 channel EEG, by spending one mere CPU second on a standard PC.