Classification of CO2 waveforms using artificial neural networks

Mohammad J. Navabi, Richard C. Watt, Kenneth C. Mylrea, Stuart R Hameroff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A computer-based system for continuous analysis of CO2 waveforms for capnography has been developed which utilizes traditional algorithmic methods to partition the waveform and artificial neural networks for waveform classification. The system uses the CO2 waveform to detect the mode of breathing during anaesthesia. As initial steps, detection of spontaneous breaths, mechanical breaths, and mechanical breaths with attempts to breath against the ventilator was attempted. The system was trained on data from 5 surgical cases and was tested on 12 cases. Of 21 breaths with signs of patient attempts to breathe against the ventilator, 18 were properly identified by the system. There were no false identifications.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference on Engineering in Medicine and Biology
PublisherPubl by IEEE
Pages1455-1456
Number of pages2
Editionpt 3
ISBN (Print)0879425598
StatePublished - 1990
EventProceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Philadelphia, PA, USA
Duration: Nov 1 1990Nov 4 1990

Other

OtherProceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CityPhiladelphia, PA, USA
Period11/1/9011/4/90

Fingerprint

Neural networks

ASJC Scopus subject areas

  • Bioengineering

Cite this

Navabi, M. J., Watt, R. C., Mylrea, K. C., & Hameroff, S. R. (1990). Classification of CO2 waveforms using artificial neural networks. In Proceedings of the Annual Conference on Engineering in Medicine and Biology (pt 3 ed., pp. 1455-1456). Publ by IEEE.

Classification of CO2 waveforms using artificial neural networks. / Navabi, Mohammad J.; Watt, Richard C.; Mylrea, Kenneth C.; Hameroff, Stuart R.

Proceedings of the Annual Conference on Engineering in Medicine and Biology. pt 3. ed. Publ by IEEE, 1990. p. 1455-1456.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Navabi, MJ, Watt, RC, Mylrea, KC & Hameroff, SR 1990, Classification of CO2 waveforms using artificial neural networks. in Proceedings of the Annual Conference on Engineering in Medicine and Biology. pt 3 edn, Publ by IEEE, pp. 1455-1456, Proceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Philadelphia, PA, USA, 11/1/90.
Navabi MJ, Watt RC, Mylrea KC, Hameroff SR. Classification of CO2 waveforms using artificial neural networks. In Proceedings of the Annual Conference on Engineering in Medicine and Biology. pt 3 ed. Publ by IEEE. 1990. p. 1455-1456
Navabi, Mohammad J. ; Watt, Richard C. ; Mylrea, Kenneth C. ; Hameroff, Stuart R. / Classification of CO2 waveforms using artificial neural networks. Proceedings of the Annual Conference on Engineering in Medicine and Biology. pt 3. ed. Publ by IEEE, 1990. pp. 1455-1456
@inproceedings{59da00aa9d5d49bb86cd825b312c4078,
title = "Classification of CO2 waveforms using artificial neural networks",
abstract = "A computer-based system for continuous analysis of CO2 waveforms for capnography has been developed which utilizes traditional algorithmic methods to partition the waveform and artificial neural networks for waveform classification. The system uses the CO2 waveform to detect the mode of breathing during anaesthesia. As initial steps, detection of spontaneous breaths, mechanical breaths, and mechanical breaths with attempts to breath against the ventilator was attempted. The system was trained on data from 5 surgical cases and was tested on 12 cases. Of 21 breaths with signs of patient attempts to breathe against the ventilator, 18 were properly identified by the system. There were no false identifications.",
author = "Navabi, {Mohammad J.} and Watt, {Richard C.} and Mylrea, {Kenneth C.} and Hameroff, {Stuart R}",
year = "1990",
language = "English (US)",
isbn = "0879425598",
pages = "1455--1456",
booktitle = "Proceedings of the Annual Conference on Engineering in Medicine and Biology",
publisher = "Publ by IEEE",
edition = "pt 3",

}

TY - GEN

T1 - Classification of CO2 waveforms using artificial neural networks

AU - Navabi, Mohammad J.

AU - Watt, Richard C.

AU - Mylrea, Kenneth C.

AU - Hameroff, Stuart R

PY - 1990

Y1 - 1990

N2 - A computer-based system for continuous analysis of CO2 waveforms for capnography has been developed which utilizes traditional algorithmic methods to partition the waveform and artificial neural networks for waveform classification. The system uses the CO2 waveform to detect the mode of breathing during anaesthesia. As initial steps, detection of spontaneous breaths, mechanical breaths, and mechanical breaths with attempts to breath against the ventilator was attempted. The system was trained on data from 5 surgical cases and was tested on 12 cases. Of 21 breaths with signs of patient attempts to breathe against the ventilator, 18 were properly identified by the system. There were no false identifications.

AB - A computer-based system for continuous analysis of CO2 waveforms for capnography has been developed which utilizes traditional algorithmic methods to partition the waveform and artificial neural networks for waveform classification. The system uses the CO2 waveform to detect the mode of breathing during anaesthesia. As initial steps, detection of spontaneous breaths, mechanical breaths, and mechanical breaths with attempts to breath against the ventilator was attempted. The system was trained on data from 5 surgical cases and was tested on 12 cases. Of 21 breaths with signs of patient attempts to breathe against the ventilator, 18 were properly identified by the system. There were no false identifications.

UR - http://www.scopus.com/inward/record.url?scp=0025544697&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0025544697&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0879425598

SP - 1455

EP - 1456

BT - Proceedings of the Annual Conference on Engineering in Medicine and Biology

PB - Publ by IEEE

ER -