Epicenter location by analysis of interictal spikes: a case study for the use of artificial neural networks in biomedical engineering.
Artificial neural network (ANN) technology is finding increasing application in medicine and biomedical engineering. This paper supplies necessary background in ANN technology for researchers unfamiliar with this rapidly emerging discipline. This introduction to ANN application is cast in the context of epileptic seizure epicenter location. This is a very real problem faced by neurosurgeons every day. Precise location of the area of excision is currently determined with a network of surgically implanted subdural electrodes. This means that the cure entails two surgical procedures: one to implant the electrode array that precisely locates the epicenter, and another to remove the epicenter. This paper outlines an experimental diagnostic software system (DSS) that uses artificial neural network (ANN) analysis of magnetoencephalographic (MEG) data to eliminate the first of these surgical procedures. The MEG recording is a quick and painless process that requires no surgery. This approach has the potential to save time, reduce patient discomfort, and eliminate a painful and potentially dangerous surgical step in the treatment procedure.
Hand
Charles
C
Jet Propulsion Laboratory, California Institute of Technology, Pasadena 91109, USA. cbear@speakeasy.net
D001698:Biomedical Engineering / Q000379:methods*; D004827:Epilepsy / Q000209:etiology* / Q000503:physiopathology*; D006801:Humans; D009415:Nerve Net / Q000502:physiology* / Q000503:physiopathology; D016571:Neural Networks, Computer