TY - JOUR
T1 - The design and hardware implementation of a low-power real-time seizure detection algorithm.
AU - Raghunathan, Shriram
AU - Gupta, Sumeet K.
AU - Ward, Matthew P.
AU - Worth, Robert M.
AU - Roy, Kaushik
AU - Irazoqui, Pedro P.
PY - 2009/10
Y1 - 2009/10
N2 - Epilepsy affects more than 1% of the world's population. Responsive neurostimulation is emerging as an alternative therapy for the 30% of the epileptic patient population that does not benefit from pharmacological treatment. Efficient seizure detection algorithms will enable closed-loop epilepsy prostheses by stimulating the epileptogenic focus within an early onset window. Critically, this is expected to reduce neuronal desensitization over time and lead to longer-term device efficacy. This work presents a novel event-based seizure detection algorithm along with a low-power digital circuit implementation. Hippocampal depth-electrode recordings from six kainate-treated rats are used to validate the algorithm and hardware performance in this preliminary study. The design process illustrates crucial trade-offs in translating mathematical models into hardware implementations and validates statistical optimizations made with empirical data analyses on results obtained using a real-time functioning hardware prototype. Using quantitatively predicted thresholds from the depth-electrode recordings, the auto-updating algorithm performs with an average sensitivity and selectivity of 95.3 +/- 0.02% and 88.9 +/- 0.01% (mean +/- SE(alpha = 0.05)), respectively, on untrained data with a detection delay of 8.5 s [5.97, 11.04] from electrographic onset. The hardware implementation is shown feasible using CMOS circuits consuming under 350 nW of power from a 250 mV supply voltage from simulations on the MIT 180 nm SOI process.
AB - Epilepsy affects more than 1% of the world's population. Responsive neurostimulation is emerging as an alternative therapy for the 30% of the epileptic patient population that does not benefit from pharmacological treatment. Efficient seizure detection algorithms will enable closed-loop epilepsy prostheses by stimulating the epileptogenic focus within an early onset window. Critically, this is expected to reduce neuronal desensitization over time and lead to longer-term device efficacy. This work presents a novel event-based seizure detection algorithm along with a low-power digital circuit implementation. Hippocampal depth-electrode recordings from six kainate-treated rats are used to validate the algorithm and hardware performance in this preliminary study. The design process illustrates crucial trade-offs in translating mathematical models into hardware implementations and validates statistical optimizations made with empirical data analyses on results obtained using a real-time functioning hardware prototype. Using quantitatively predicted thresholds from the depth-electrode recordings, the auto-updating algorithm performs with an average sensitivity and selectivity of 95.3 +/- 0.02% and 88.9 +/- 0.01% (mean +/- SE(alpha = 0.05)), respectively, on untrained data with a detection delay of 8.5 s [5.97, 11.04] from electrographic onset. The hardware implementation is shown feasible using CMOS circuits consuming under 350 nW of power from a 250 mV supply voltage from simulations on the MIT 180 nm SOI process.
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U2 - 10.1088/1741-2560/6/5/056005
DO - 10.1088/1741-2560/6/5/056005
M3 - Article
C2 - 19717893
AN - SCOPUS:77957686139
SN - 1741-2560
VL - 6
SP - 56005
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 5
ER -