A micropower support vector machine based seizure detection architecture for embedded medical devices.
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.
Shoeb
Ali
A
Massachusetts Institute of Technology, Boston, MA 02139, USA. ashoeb@mit.edu
Carlson
Dave
D
Panken
Eric
E
Denison
Timothy
T
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D011211:Electric Power Supplies; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis; D004867:Equipment Design; D006801:Humans; D008328:Man-Machine Systems; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D012640:Seizures / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors