RT Book, Section
A1 Gardner, Andrew
A1 Stead, S. Matt
A1 Worrell, Greg A.
A2 Sirven, Joseph I.
A2 Stern, John M.
SR Print(0)
ID 1103048537
T1 Automated Analysis of EEG
T2 Atlas of Video-EEG Monitoring
YR 2011
FD 2011
PB McGraw-Hill Education
PP New York, NY
SN 9780071597425
LK neurology.mhmedical.com/content.aspx?aid=1103048537
RD 2024/04/19
AB The  electroencephalogram  (EEG)  provides  a  real-time  measure  of  brain  electrical  activity  and  has  proven  to  be  useful  in  a  wide  range  of  clinical  settings,  for  example,  evaluation  of  seizures,  mental  status  change,  coma,  and  classification  of  epilepsy.1,2  The  clinical  applications  of  continuous  EEG  monitoring  can  involve  recordings  from  scalp  or  intracranial  electrodes  obtained  in  the  hospital  (e.g.,  video  scalp  and  intracranial  EEG  [IEEG]  monitoring),  on  an  outpatient  basis  (routine  scalp  EEG),  and  even  in  nonmedical  settings  (ambulatory  EEG).  More  recently,  a  cranially  implanted  device  (the  RNS  system,  NeuroPace  Inc.)  using  continuous  IEEG  for  real-time  seizure  detection  and  electrical  stimulation  to  abort  seizures  is  undergoing  clinical  trial.3  This  broad  range  of  clinical  applications  underscores  the  usefulness  of  EEG  for  studying  brain  dysfunction  and  highlights  the  potential  applications  of  automated  EEG  analysis.  In  this  chapter,  we  give  an  overview  of  automated  detection  of  epileptiform  activity.  The  primary  goal  is  to  discuss  epileptiform  event  detection  in  the  context  of  modern  data  mining  and  pattern  recognition.4–6  The  chapter  is  not  intended  to  be  a  comprehensive  review  of  epileptiform  spike  and  seizure  detection.7–10