Towards real-time in-implant epileptic seizure prediction.
We present an architecture of an epileptic seizure prediction system suitable for an implantable implementation. The microsystem comprises a neural interface, a spectral analysis processor and an artificial neural network (ANN). The neural interface and the spectral analysis processor have been prototyped in a 0.35 microm CMOS technology with experimental results are presented. The wavelet-based artificial neural network predicts the onsets of seizure up to two minutes before their occurrence in an in-vitro epilepsy model using a mouse hippocampal brain slice with recurrent spontaneous seizures.
Aziz
Joseph N Y
JN
Department of Electrical & Computer Engineering, University of Toronto, ON, Canada.
Karakiewicz
Rafal
R
Genov
Roman
R
Bardakjian
Berj L
BL
Derchansky
Miron
M
Carlen
Peter L
PL
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D051379:Mice; D008838:Microcomputers; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors