Neural net based prediction of depth of anesthesia

Abinash Nayak; Jitendran Muthuswamy; Rob J. Roy

Neural net based prediction of depth of anesthesia

Abinash Nayak
, Jitendran Muthuswamy
, Rob J. Roy

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English (US)
Title of host publication	Artificial Neural Networks in Engineering - Proceedings (ANNIE'94)
Place of Publication	New York, NY, United States
Publisher	ASME
Pages	663-668
Number of pages	6
Volume	4
State	Published - 1994
Externally published	Yes
Event	Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94) - St. Louis, MO, USA Duration: Nov 13 1994 → Nov 16 1994

Other

Other	Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)
City	St. Louis, MO, USA
Period	11/13/94 → 11/16/94

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Neural net based prediction of depth of anesthesia",

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.",

author = "Abinash Nayak and Jitendran Muthuswamy and Roy, \{Rob J.\}",

year = "1994",

language = "English (US)",

volume = "4",

pages = "663--668",

booktitle = "Artificial Neural Networks in Engineering - Proceedings (ANNIE'94)",

publisher = "ASME",

note = "Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94) ; Conference date: 13-11-1994 Through 16-11-1994",

}

TY - CHAP

T1 - Neural net based prediction of depth of anesthesia

AU - Nayak, Abinash

AU - Muthuswamy, Jitendran

AU - Roy, Rob J.

PY - 1994

Y1 - 1994

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/0028730718

UR - https://www.scopus.com/pages/publications/0028730718#tab=citedBy

M3 - Chapter

AN - SCOPUS:0028730718

VL - 4

SP - 663

EP - 668

BT - Artificial Neural Networks in Engineering - Proceedings (ANNIE'94)

PB - ASME

CY - New York, NY, United States

T2 - Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)

Y2 - 13 November 1994 through 16 November 1994

ER -

Neural net based prediction of depth of anesthesia

Abstract

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ASJC Scopus subject areas

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