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Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library. The machine-learning based architecture was fully implemented in the micropower domain, demonstrating feasibility for an embedded detector in implantable systems.

Original publication




Journal article


Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Publication Date





4202 - 4205


Massachusetts Institute of Technology, Boston, MA 02139, USA.


Humans, Epilepsy, Seizures, Electroencephalography, Models, Statistical, Equipment Design, Man-Machine Systems, Algorithms, Models, Theoretical, Time Factors, Artificial Intelligence, Signal Processing, Computer-Assisted, Pattern Recognition, Automated, Adult, Electric Power Supplies