Neural net based prediction of depth of anesthesia

Abinash Nayak, Jitendran Muthuswamy, Rob J. Roy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The depth anesthesia were predicted with two different approaches using the Artificial Neutral Networks (ANN). In one approach, parameters derived from the autoregressive modeling of the Midlatency Evoked potentials (MLAEP) were used. The other approach involved the use of bispectral parameters derived from the EEG. As a result, it is shown that the ANN can be a useful tool in predicting the depth of the anesthesia. However, further tests are required to demonstrate the clinical viability of ANN.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks in Engineering - Proceedings (ANNIE'94)
Place of PublicationNew York, NY, United States
PublisherASME
Pages663-668
Number of pages6
Volume4
StatePublished - 1994
Externally publishedYes
EventProceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94) - St. Louis, MO, USA
Duration: Nov 13 1994Nov 16 1994

Other

OtherProceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)
CitySt. Louis, MO, USA
Period11/13/9411/16/94

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Neural net based prediction of depth of anesthesia'. Together they form a unique fingerprint.

  • Cite this

    Nayak, A., Muthuswamy, J., & Roy, R. J. (1994). Neural net based prediction of depth of anesthesia. In Artificial Neural Networks in Engineering - Proceedings (ANNIE'94) (Vol. 4, pp. 663-668). ASME.