In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-Si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate.
['Himanshu S. Markandeya', 'Georgios Karakonstantis', 'Shriram Raghunathan', 'Pedro P. Irazoqui', 'Kaushik Roy']
Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection