Predicting depth of anesthesia using bispectral parameters in neural networks

Jitendran Muthuswamy; Rob J. Roy

Predicting depth of anesthesia using bispectral parameters in neural networks

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

Abstract

Features like the spectral edge and median frequency derived from power spectrum of the EEG have so far failed to show any consistent changes with the depth of anesthesia. One of the disadvantages of using power spectrum is that it suppresses phase information in the signal. A third order spectrum or bispectrum preserves phase information. A bispectral parameter called bicoherence index was derived from the EEG prior to a tail clamp. Using the bicoherence index and the estimated MAC level of the dog at that time a neural network was able to correctly classify all the 36 data points from a test group corresponding to either an awake or an asleep dog.

Original language	English (US)
Title of host publication	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publisher	IEEE
Pages	1087-1088
Number of pages	2
Volume	16
Edition	pt 2
State	Published - 1994
Externally published	Yes
Event	Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2) - Baltimore, MD, USA Duration: Nov 3 1994 → Nov 6 1994

Other

Other	Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2)
City	Baltimore, MD, USA
Period	11/3/94 → 11/6/94

ASJC Scopus subject areas

Bioengineering

Cite this

Muthuswamy, J & Roy, RJ 1994, Predicting depth of anesthesia using bispectral parameters in neural networks. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. pt 2 edn, vol. 16, IEEE, pp. 1087-1088, Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2), Baltimore, MD, USA, 11/3/94.

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year = "1994",

language = "English (US)",

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AB - Features like the spectral edge and median frequency derived from power spectrum of the EEG have so far failed to show any consistent changes with the depth of anesthesia. One of the disadvantages of using power spectrum is that it suppresses phase information in the signal. A third order spectrum or bispectrum preserves phase information. A bispectral parameter called bicoherence index was derived from the EEG prior to a tail clamp. Using the bicoherence index and the estimated MAC level of the dog at that time a neural network was able to correctly classify all the 36 data points from a test group corresponding to either an awake or an asleep dog.

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ER -

Predicting depth of anesthesia using bispectral parameters in neural networks

Abstract

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ASJC Scopus subject areas

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