Low-power high-accuracy seizure detection algorithms for neural implantable platforms

Abstract

Neural interfaces are systems operating at the intersection of the nervous system and an internal or external device. Neuro-stimulator is one of the most important neural interfaces used to help those who experience epileptic seizures. To use this stimulator efficiently, seizure should be detected at the right time. Seizure detection is basically founded on digital signal processing by monitoring certain features of the intracranial electroencephalogram. Many of the previous researches are directed to study the detection efficacies using different systems, however, a few of them study the feasibility of implementing these systems over a computationally limited power implantable platforms. In this paper, five time-domain features and three wavelet-domain features are investigated. Following that, a high accuracy seizure detection algorithm is presented with efficient power consumption which makes it suitable for implantable neural systems. The experiment results show that the presented method achieves a sensitivity, specificity, and accuracy of 92.64%, 99.29%, and 99.16% respectively for long-term iEEG seizure detection. The area and power results are obtained from implementing the algorithms on Xilinx Spartan-6 XC6SLX45T FPGA.