The implementation of a low-power biomedical signal processor for real-time epileptic seizure detection on absence animal models

Tsan Jieh Chen, Herming Chiueh, Sheng Fu Liang, Shun Ting Chang, Chi Jeng, Yu Cheng Hsu, Tzu Chieh Chien

Research output: Contribution to journalArticle

24 Citations (Scopus)

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 μm 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.

Original languageEnglish
Article number6081950
Pages (from-to)613-621
Number of pages9
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume1
Issue number4
DOIs
Publication statusPublished - 2011 Dec 1

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'The implementation of a low-power biomedical signal processor for real-time epileptic seizure detection on absence animal models'. Together they form a unique fingerprint.

  • Cite this