Analysis of Quantitative EEG with Artificial Neural Networks and Discriminant Analysis – A Methodological Comparison

Artificial neural networks (ANN) are widely used to solve problems of differentiating between groups. However, serious comparisons of this method with the traditional procedure for such tasks (discriminant analysis) are rare. Discussing the results of both methods with the example of highly topical data, we try to demonstrate advantages and drawbacks of both methods. For this purpose, quantitative EEGs of 78 alcoholics were investigated in order to determine whether it is possible to predict relapse of these patients at the beginning of treatment. ANN software is available in Kassel (Institute for Computer Sciences and Mathematics).

[1]  James M. Hutchinson,et al.  A radial basis function approach to financial time series analysis , 1993 .

[2]  P. Rappelsberger,et al.  CLASSIFICATION OF EEG OF SCHIZOPHRENICS AND DEPRESSIVES WITH ARTIFICIAL NEURAL NETWORKS , 1997 .

[3]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[4]  G Reibnegger,et al.  Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[5]  C. Lee Giles,et al.  Pruning recurrent neural networks for improved generalization performance , 1994, IEEE Trans. Neural Networks.

[6]  D F Sittig,et al.  A parallel implementation of the backward error propagation neural network training algorithm: experiments in event identification. , 1992, Computers and biomedical research, an international journal.

[7]  F. Mandelli,et al.  Classification of patients affected by multiple myeloma using a neural network software , 1994, European journal of haematology.

[8]  K. J. Dalton,et al.  Artificial neural networks for decision support in clinical medicine. , 1995, Annals of medicine.

[9]  Thomas P. Vogl,et al.  Rescaling of variables in back propagation learning , 1991, Neural Networks.

[10]  A. Heinz,et al.  Signalkomplexität versus Spektralparameter in EEG-Zeitreihen von psychiatrischen Patienten: Eine retrospektive Klassifikationsstudie , 1995 .

[11]  William G. Baxt,et al.  Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion , 1990, Neural Computation.

[12]  Tariq Samad,et al.  Towards the Genetic Synthesisof Neural Networks , 1989, ICGA.

[13]  P W Macfarlane,et al.  Neural networks for classification of ECG ST-T segments. , 1992, Journal of electrocardiology.

[14]  Ferdinand Hergert,et al.  Improving model selection by nonconvergent methods , 1993, Neural Networks.

[15]  Brad Warner,et al.  Understanding Neural Networks as Statistical Tools , 1996 .

[16]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[17]  Terrence J. Sejnowski,et al.  Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[18]  B. Klöppel,et al.  Application of neural networks for EEG analysis. Considerations and first results. , 1994, Neuropsychobiology.

[19]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[20]  H Fujita,et al.  Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull's-eye images. , 1992, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[21]  秦 浩起,et al.  Characterization of Strange Attractor (カオスとその周辺(基研長期研究会報告)) , 1987 .

[22]  J N Weinstein,et al.  Neural computing in cancer drug development: predicting mechanism of action. , 1992, Science.

[23]  Geoffrey E. Hinton Connectionist Learning Procedures , 1989, Artif. Intell..

[24]  B. Klöppel,et al.  Neural networks as a new method for EEG analysis. A basic introduction. , 1994, Neuropsychobiology.

[25]  Bert Klöppel Stabilität und Kapazität neuronaler Netzwerke: am Beispiel der EEG-Analyse , 1994 .

[26]  R. D'Agostino,et al.  A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. , 1995, Journal of investigative medicine : the official publication of the American Federation for Clinical Research.

[27]  J. Heinsimer,et al.  Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence. , 1991, American journal of clinical pathology.

[28]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[29]  J. Kippenhan,et al.  Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects. , 1992, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[30]  C E Floyd,et al.  An artificial neural network for lesion detection on single-photon emission computed tomographic images. , 1992, Investigative radiology.

[31]  Lashon B. Booker,et al.  Representing Attribute-Based Concepts in a Classifier System , 1990, FOGA.

[32]  Vincent G. Sigillito,et al.  An Interaction between Auxiliary Knowledge and Hidden Nodes on Time to Convergence , 1989 .

[33]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[34]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .