A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications

Shriram Raghunathan, Sumeet K. Gupta, Himanshu S. Markandeya, Kaushik Roy, Pedro P. Irazoqui

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy.

Original languageEnglish (US)
Pages (from-to)106-117
Number of pages12
JournalJournal of Neuroscience Methods
Issue number1
StatePublished - Oct 30 2010

All Science Journal Classification (ASJC) codes

  • General Neuroscience


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