title: Automatic detection of intraoperative neurological injury.
abstract: Neurological injuries occurring during high-risk surgical procedures can be detected by monitoring intraoperative evoked potential signals. In this communication, an automatic injury detection algorithm is proposed in which the EP signal is modeled as a pole-zero filter and then the model parameters are applied as inputs to a classifier type neural network. A recognition rate of 96% is achieved using an experimental model of brain injury.
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology, Madras, India.
authors.forename: L V
authors.initials: LV
authors.lastname: Williams
authors.forename: C
authors.initials: C
authors.lastname: Eswaran
mesh_terms: D000465:Algorithms; D000818:Animals; D001930:Brain Injuries / Q000175:diagnosis* / Q000517:prevention & control; D002415:Cats; D005073:Evoked Potentials, Somatosensory; D008959:Models, Neurological; D016343:Monitoring, Intraoperative / Q000295:instrumentation*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted