This paper presents the design and implementation of a real-time epilepsy detection filter that is suitable for closed-loop seizure suppression. The design aims to minimize the detection delay, while a reasonable average detection rate is obtained. The filter is based on a complex Morlet wavelet and uses an adaptive thresholding strategy for the seizure discrimination. This relatively simple configuration allows the algorithm to run on a cheap and readily available microprocessor prototyping platform. The performance of the filter is verified using both in vivo real-time measurements as well as simulations over a pre-recorded EEG dataset (29.75 hours with 1914 seizures). An average detection delay of 492 ms is achieved with a sensitivity of 96.03% and a specificity of 93.60%.,
10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014
Department of Neuroscience

van Dongen, M, Karapatis, A, Kros, L, Eelkman Rooda, O.H.J, Seepers, R.M, Strydis, C, … Serdijn, W. (2014). An implementation of a wavelet-based seizure detection filter suitable for realtime closed-loop epileptic seizure suppression. Presented at the 10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014. doi:10.1109/BioCAS.2014.6981773