Identification of mediator specific cardiovascular waveforms using a back propagation neural network

Rayburn, Daniel B.; Januszkiewicz, Adolph J.; Ripple, Gary R.; Truwit, John; Summery, Harold F.; Young, Thomas R.; Klimasauskas, C.C.; Lee, Jamie M.; Snapper, James R.

doi:10.1016/0893-6080(91)90049-b

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…One family of such algorithms comprises artificial neural networks. These algorithms, inspired by a model of the workings of the human brain, have recently found application in fields as diverse as sonar signal processing (Gorman & Sejnowski 1988) and medical diagnostics (Rayburn et al 1991). Neural networks are increasingly being used in astronomy (e.g.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks and the Classification of Active Galactic Nucleus Spectra

Rawson¹,

Bailey²,

Francis³

1996

Publ. Astron. Soc. Aust.

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The use of Artificial Neural Networks (ANNs) as a classifier of digital spectra is investigated. Using both simulated and real data, it is shown that neural networks can be trained to discriminate between the spectra of different classes of active galactic nucleus (AGN) with realistic sample sizes and signal-to-noise ratios. By working in the Fourier domain, neural nets can classify objects without knowledge of their redshifts.

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Section: Introductionmentioning

confidence: 99%

Neural Networks and the Classification of Active Galactic Nucleus Spectra

Rawson¹,

Bailey²,

Francis³

1996

Publ. Astron. Soc. Aust.

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Neural Net Demonstrators

Taylor¹

1993

The Promise of Neural Networks

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Analytical methods to differentiate similar electroencephalographic spectra: neural network and discriminant analysis

Veselis

Reinsel

Wroński

1993

J Clin Monitor Comput

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Differences in electroencephalographic (EEG) power spectra obtained under similar, but not identical, conditions may be difficult to discern using standard techniques. Statistical analysis may not be useful because of the large number of comparisons necessary. Visual recognition of differences also may be difficult. A new technique, neural network analysis, has been used successfully in other problems of pattern recognition and classification. We examined a number of methods of classifying similar EEG data: standard statistical analysis (analysis of variance), visual recognition, discriminant analysis, and neural network analysis. Twenty-nine volunteers received either thiopental (n = 9), midazolam (n = 10), or propofol (n = 10) in sedative doses in 3 different studies. These drugs produced very similar changes in the EEG power spectra. Except for beta 2 power during thiopental infusion, differences between drugs could not be detected using analysis of variance. Visual categorization was correct in 72% of the baseline EEGs, 70% of thiopental EEGs, 27% of propofol EEGs, and 46% of midazolam EEGs. A classification neural network (Learning Vector Quantization network) containing a Kohonen hidden layer was able to successfully classify 57 of 58 EEG samples (of 4 minutes' duration). Discriminant analysis had a similar rate of success. This level of performance was achieved by dividing the EEG power spectrum from 1 to 30 Hz into 15 2-Hz bandwidths. When the EEG power spectrum was divided into the "classical" frequency bandwidths (alpha, beta 1, beta 2, theta, delta), both neural network and discriminant analysis performance deteriorated. By training the network using only certain inputs we were able to identify drug-specific bandwidths that seemed to be important in correct classification. We conclude that propofol, thiopental, and midazolam produce different effects on the EEG and that both neural network and discriminant analysis are useful in identifying these differences. We also conclude that EEG spectra should be analyzed without using classical EEG bands (alpha, beta, etc.). Additionally, neural networks can be used to identify frequency bands that are "important" in specific drug effects on the EEG. Once a classification algorithm is obtained using either a neural network or discriminant analysis, it could be used as an on-line monitor to recognize drug-specific EEG patterns.

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Identification of mediator specific cardiovascular waveforms using a back propagation neural network

Cited by 3 publications

References 6 publications

Neural Networks and the Classification of Active Galactic Nucleus Spectra

Neural Networks and the Classification of Active Galactic Nucleus Spectra

Neural Net Demonstrators

Analytical methods to differentiate similar electroencephalographic spectra: neural network and discriminant analysis

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