Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection

The fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of two FD-based methodologies are compared in terms of their ability to detect the onset of epileptic seizures in scalp EEG. The FD algorithms used is Katzpsilas, which has been broadly utilized in the EEG analysis literature, and the k-nearest neighbor (k-NN), which is applied in this study in a time series sense for the first time. 244.9 hours of EEG recordings, including 16 seizures in 3 patients, were analyzed. Both approaches achieved 100% sensitivity with a false positive rate of 0.85 FP/h for the k-NN algorithm and 1 FP/h for Katzpsilas algorithm. The corresponding detection delays were 6.5 s and 10.5 s on the average, respectively. The k-NN algorithm seems to outperform Katzpsilas algorithm. Results are satisfactory in comparison to other methodologies applied on scalp EEG and proposed in the literature.