Navegar

Colección

Datos Estadísticas

Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

Previous studies from different laboratories have suggested that qEEG could be useful for distinguishing dementia from normality. Our aims were: (1) to study the ability of qEEG to distinguish dementia among different pathological conditions in ambulatory settings; (2) to compare the ability of classical statistical analysis and of neural networks in classifying qEEG data. We were able to obtain a multiple discriminant function using a training set of patients, which classified correctly more than 91% of the qEEGs from an independent group of patients, with less than 5% of false positives. Kohonen’s neural network was trained with the same set of patients. This unsupervised learning artificial neural network performed the classification of the independent sample with an accuracy comparable to that of the multiple discriminant function. Our results suggest that the use of unsupervised learning algorithms could be an interesting alternative in the classification of data obtained from psychiatric patients where definition of their clinical profile is not always a simple task. © 1996 S. Karger AG, Basel.

Registro:

Documento: Artículo
Título:Classification of quantitative eeg data by an artificial neural network: A preliminary study
Autor:Riquelme, L.A.; Zanuto, B.S.; Murer, M.G.; Lombardo, R.J.
Filiación:Laboratorio de Neurofisiología, Departamento de Fisiología y Biofísica, Facultad de Medicina, Argentina
Laboratorio de Redes Neuronales, Facultad de Ingenieria, Argentina
Departamento de Ciencias Biológicas, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
Palabras clave:Artificial neural networks; Dementia; Multiple discriminant function; qEEG; adult; aged; algorithm; anxiety neurosis; article; artificial neural network; clinical trial; controlled clinical trial; controlled study; dementia; depression; discriminant analysis; electroencephalogram; female; human; major clinical study; male; mental patient; priority journal; statistical analysis; Adult; Aged; Anxiety; Brain; Dementia; Depressive Disorder; Electroencephalography; Female; Humans; Male; Middle Aged; Neural Networks (Computer)
Año:1996
Volumen:33
Número:2
Página de inicio:106
Página de fin:112
DOI: http://dx.doi.org/10.1159/000119259
Título revista:Neuropsychobiology
Título revista abreviado:Neuropsychobiology
ISSN:0302282X
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0302282X_v33_n2_p106_Riquelme

Referencias:

  • Gasser, T., Bacher, P., Steinberg, H., Test-retest reliability of spectral parameters of the EEG (1985) Electroencephalogr Clin Neurophysiol, 60, pp. 312-319
  • Oken, B.S., Chiappa, K.M., Short-term variability in EEG frequency analysis (1988) Electroencephalogr Clin Neurophysiol, 69, pp. 191-198
  • Oken, B.S., Chiappa, K.H., Statistical issues concerning computerized analysis of brainwave topography (1986) Ann Neurol, 19, pp. 493-494
  • Gasser, T., Bacher, P., Mocks, J., Transformation towards the normal distribution of broad band spectral parameters of the EEG (1982) Electroencephalogr Clin Neurophysiol, 53, pp. 119-124
  • John, E.R., Prichcp, L.S., Friedman, J., Easton, P., Neurometric topographic mapping of EEG and evoked potential features: Application to clinical diagnosis and cognitive evaluation (1989) Topographic Brain Mapping of EEG and Evoked Potentials, pp. 90-117. , Maurer K (cd), Berlin, Springer
  • Nuwer, M.R., Quantitative EEG: 1, Techniques and problems of frequency analysis and topographic mapping (1988) J Clin Neurophysiol, 5, pp. 1-43
  • Duffy, F.H., Bartels, P.H., Neff, R., A response to Oken and Chiappa (1986) Ann Neurol, 19, pp. 494-497
  • Kippenham, J.S., Barker, W.W., Pascal, S., Nagel, J., Duara, R., Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects (1992) J Nuct Med, 33, pp. 1459-1467
  • Wu, F.Y., Slater, J.D., Regional cerebral blood flow estimation by neural network based parametric regression analysis (1993) Int J Biomed Corn-Put, 33, pp. 119-128
  • Astion, M.L., Wilding, P., The application of backpropagation neural networks to problems in pathology and laboratory medicine (1992) Arch Pathol Lab Med, 116, pp. 995-1001
  • Wu, F.Y., Slater, J.D., Ramsay, R.E., Neural network approach in multichannel auditory event related potential analysis (1994) Int J Biomed Corn-Put, 35, pp. 157-168
  • Klöppel, B., Classification by neural networks of evoked potentials (1994) Neuropsvchobiology, 29, pp. 47-52
  • Prichep, L.S., John, E.R., Ferris, S.H., Reisberg, B., Almas, M., Alper, K., Cancro, R., QEEG correlates of cognitive deterioration in the elderly (1994) Ncuro-Biol Aging, 15, pp. 85-90
  • Robinson, D.J., Merskey, H., Blume, W.T., Fry, R., Williamson, P.C., Hachinski, V.C., EEG as an aid in the exclusion of Alzheimer disease (1994) Arch Neurol, 51, pp. 280-284
  • Szclics, B., Grond, M., Herholz, K., Kessler, J., Wullen, T., Hciss, W.D., QEEG mapping and PET in Alzheimer disease (1992) J Neurol Sei, 110, pp. 46-56
  • Riekkinen, P., Buzsaki, G., Riekkinen, P., Soininen, H., Partanen, J., Review article: The cholinergic system and EEG slow waves (1991) Electroencephalogr Clin Neurophysiol, 78, pp. 89-96
  • Riquelme, L.A., Murcr, M.G., Zanutto, B.S., Classification of quantitative EEG data by an artificial neural network (1993) Proc 4Th Symp Int Soc Brain Electromagnet Topogr, , La Havana
  • Morrison, D.F., (1976) Multivariate Statistical Methods, , ed 2, New York, McGraw Hill
  • Hertz, J., Krogh, A., Palmer, R.G., (1991) Introduction to the Theory of Neural Computation, , Redwood City, Addision-Wesley
  • Kohonen, T., (1988) Self-Organization and Associative Memory, , ed 2, Berlin, Springer
  • Ferrcro, A.R., Riquelme, L.A., Murer, M.G., Lombardo, R., Indicadores cartográficos de la orga-nicidad en psiquiatría (1993) Rev Neurofisiol Clin, 6 (3), pp. 13-19
  • Pentlilla, M., Partanen, V.V., Soinincn, H., Riekkinen, P.J., Quantitative analysis of occipital EEG in different stages of Alzheimers disease (1985) Electroencephalogr Clin Neurophysiol, 60, pp. 1-6
  • Erer, R., Saletu, B., Klöppel, B., Semlitsch, H.V., Werner, H., Discrimination between demented patients an normals based on topographic EEG slow wave activity: Comparasion between z statistics, discriminant analysis and artificial neural networks classifiers (1994) Electroencephalogr Clin Neurophysiol, 91, pp. 108-117
  • Pritchard, W.S., Duk, D.W., Coburn, K.L., Moore, N.C., Tucker, K.A., Jann, M.W., Hostetler, R.M., EEG based, neural-net predictive classification of Alzheimer’s disease versus control subjects is augmented by non-linear EEG measures (1994) Electrcencephalogr Clin Ncurophysiol, 91, pp. 118-130
  • Rumelhart, D.E., McClelland, J.L., (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, , Cambridge, MIT Press
  • Mendez, M.F., Mastri, A.R., Sung, J.H., Frey, W.H., Clinically diagnosed Alzheimer disease: Neuropathologie findings in 650 cases (1992) Alzheimer Dis Assoc Disord, 6, pp. 35-43

Citas:

---------- APA ----------
Riquelme, L.A., Zanuto, B.S., Murer, M.G. & Lombardo, R.J. (1996) . Classification of quantitative eeg data by an artificial neural network: A preliminary study. Neuropsychobiology, 33(2), 106-112.
http://dx.doi.org/10.1159/000119259
---------- CHICAGO ----------
Riquelme, L.A., Zanuto, B.S., Murer, M.G., Lombardo, R.J. "Classification of quantitative eeg data by an artificial neural network: A preliminary study" . Neuropsychobiology 33, no. 2 (1996) : 106-112.
http://dx.doi.org/10.1159/000119259
---------- MLA ----------
Riquelme, L.A., Zanuto, B.S., Murer, M.G., Lombardo, R.J. "Classification of quantitative eeg data by an artificial neural network: A preliminary study" . Neuropsychobiology, vol. 33, no. 2, 1996, pp. 106-112.
http://dx.doi.org/10.1159/000119259
---------- VANCOUVER ----------
Riquelme, L.A., Zanuto, B.S., Murer, M.G., Lombardo, R.J. Classification of quantitative eeg data by an artificial neural network: A preliminary study. Neuropsychobiology. 1996;33(2):106-112.
http://dx.doi.org/10.1159/000119259