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Title: Neurometric assessment of intraoperative anesthetic

Abstract

The present invention is a method and apparatus for collecting EEG data, reducing the EEG data into coefficients, and correlating those coefficients with a depth of unconsciousness or anesthetic depth, and which obtains a bounded first derivative of anesthetic depth to indicate trends. The present invention provides a developed artificial neural network based method capable of continuously analyzing EEG data to discriminate between awake and anesthetized states in an individual and continuously monitoring anesthetic depth trends in real-time. The present invention enables an anesthesiologist to respond immediately to changes in anesthetic depth of the patient during surgery and to administer the correct amount of anesthetic. 7 figs.

Inventors:
;
Issue Date:
Research Org.:
Battelle Memorial Institute, Columbus, OH (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
672565
Patent Number(s):
5775330
Application Number:
PAN: 8-681,196
Assignee:
Battelle Memorial Inst., Richland, WA (United States)
DOE Contract Number:  
AC06-76RL01830
Resource Type:
Patent
Resource Relation:
Other Information: PBD: 7 Jul 1998
Country of Publication:
United States
Language:
English
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; ANESTHESIA; ANESTHETICS; ELECTROENCEPHALOGRAPHY; SURGERY; NEURAL NETWORKS; MONITORING; REAL TIME SYSTEMS

Citation Formats

Kangas, L J, and Keller, P E. Neurometric assessment of intraoperative anesthetic. United States: N. p., 1998. Web.
Kangas, L J, & Keller, P E. Neurometric assessment of intraoperative anesthetic. United States.
Kangas, L J, and Keller, P E. Tue . "Neurometric assessment of intraoperative anesthetic". United States.
@article{osti_672565,
title = {Neurometric assessment of intraoperative anesthetic},
author = {Kangas, L J and Keller, P E},
abstractNote = {The present invention is a method and apparatus for collecting EEG data, reducing the EEG data into coefficients, and correlating those coefficients with a depth of unconsciousness or anesthetic depth, and which obtains a bounded first derivative of anesthetic depth to indicate trends. The present invention provides a developed artificial neural network based method capable of continuously analyzing EEG data to discriminate between awake and anesthetized states in an individual and continuously monitoring anesthetic depth trends in real-time. The present invention enables an anesthesiologist to respond immediately to changes in anesthetic depth of the patient during surgery and to administer the correct amount of anesthetic. 7 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1998},
month = {7}
}