The Implementation of a Low-Power Biomedical Signal Processor for Real-Time Epileptic Seizure Detection on Absence Animal Models

Abstract

Epilepsy is one of the most common neurological disorders, with a worldwide prevalence of approximately 1%. A considerable portion of epilepsy patients cannot be treated sufficiently by today\'s available therapies. Implantable closed-loop neurostimulation is an innovative and effective method for seizure control. A real-time seizure detector is the kernel of a closed-loop seizure controller. In this paper, a low-power biomedical signal processor based on reduced instruction set computer (RISC) architecture for real-time seizure detection is implemented to achieve low-power consumption and perform continuous and real-time processing. The low-power processor is implemented in a 0.18 mu m complementary-metal-oxide semiconductor technology to verify functionality and capability. The measurement results show the implemented processor can reduce over 90% power consumption compared with our previous prototype, which was implemented on an enhanced 8051 microprocessor. This seizure detector was applied to the continuous EEG signals of four Long-Evans rats with spontaneous absence seizures. It also processed 24 h long-term and uninterrupted EEG sequence. The developed seizure detector can be applied for online seizure monitoring and integrated with an electrical stimulator to perform a closed-loop seizure controller in the future.