Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

Abe, Jair Minoro; Lopes, Helder Frederico da Silva; Anghinah, Renato

doi:10.1590/S1980-57642008DN10300004

Abstract

EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

Key words:
EEG; Alzheimer disease; pattern recognition; artificial neural network; paraconsistent logic.

Resumo

A análise visual de EEG tem se mostrado útil na ajuda de diagnóstico de DA, sendo indicado em alguns protocolos clínicos. Porém, tal análise está sujeita à imprecisão inerente de equipamentos, movimentos do paciente, registros elétricos e variação individual da análise visual do médico. Objetivos: Utilizar a Rede Neural Artificial Paraconsistente para saber como determinar um grau de certeza no diagnóstico da doença de Alzheimer provável. Métodos: Dez pacientes com doença de Alzheimer provável e 10 controles foram submetidos ao registro de exames de EEG durante a vigília em repouso. Considerou-se como padrão normal de um paciente, a atividade de base entre 8,0 Hz a 12,0 Hz, permitindo uma variação de 0.5 Hz. Resultados: A RNAP foi capaz de reconhecer ondas que pertencem à banda Alfa como banda Alfa com evidência favorável de 0.30 e evidência contrária de 0.19, enquanto ondas não pertencentes ao padrão Alfa, foi obtido uma evidência favorável média de 0.19 e evidência contrária de 0.32, mostrando que a RNAP foi eficiente para reconhecer ondas Alfa, o que leva a uma concordância com o diagnóstico clínico de 80%. Conclusões: RNAP pode ser ferramenta promissora para manipular análise de EEG, tendo em mente ambas considerações: o interesse crescente de especialistas em análise visual de EEG e a capacidade da RNAP tratar diretamente dados imprecisos, inconsistentes e paracompletos, fornecendo uma interessante análise quantitativa.

Palavras-chave:
EEG; doença de Alzheimer; rede neural artificial; reconhecimento de padrões; lógica paraconsistente.

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References

¹
Duffy FH, Albert MS, Mcnulty G, Garvey AJ. Age differences in brain electrical activity of healthy subjects. Ann Neural 1984;16:430-438.
²
Nuwer MR, Comi G, Emerson R, et al. IFCN standards for digital recording of clinical EEG. Electroencephalogr Clin Neurophysiol 1998;106:259-261.
³
Nitrini R, Caramelli P, Bottino CM, Damasceno BP, Brucki SM, Anghinah R. Academia Brasileira de Neurologia. Diagnosis of Alzheimer's disease in Brazil: diagnostic criteria and auxiliary tests. Recommendations of the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology. Arq Neuropsiquiatr 2005;63:713-719.
⁴
Montenegro MA, Cendes F, Guerreiro MM, Guerreiro CAM, editors. EEG na Prática Clínica, São Paulo: Lemos Editorial; 2001.
⁵
Abe JM. Fundamentos da lógica anotada. Thesis, Faculdade Filosofia Letras e Ciências Humanas da Universidade de São Paulo, São Paulo, Brazil, 1992.
⁶
Anghinah R. Estudo da densidade espectral e da coerência do eletrencefalograma em indivíduos adultos normais e com doença de Alzheimer provável. Thesis, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil, 2003.
⁷
Da Costa NCA, Abe JM, Silva Filho JI, Murolo AC, Leite CFS. Lógica Paraconsistente Aplicada. São Paulo: Editora Atlas; 1999.
⁸
Da Silva Filho JI, Abe JM. Fundamentos das redes neurais paraconsistentes: destacando aplicações em neurocomputação. São Paulo: Editora Arte & Ciência; 2001.
⁹
Erganian A, Mahmoudi B. Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface. Med Biol Eng Comput 2005;43:296-305.
¹⁰
Fausett L. Fundamentals of neural network architectures, algorithms, and applications, US Ed edition, New York: Prentice Hall, 1994.
¹¹
Subasi A, Alkan A, Koklukaya E, Kiymik MK. Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing, Neural Netw 2005;18:985-997.
¹²
Ventouras EM, Monoyou EA, Ktonas PY, et al. 25 Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study. Comput Methods Programs Biomed 2005;78:191-207.
¹³
Miller A. The application of neural networks to imaging and signal processing in astronomy and medicine. Thesis, Faculty of Science, Department of Physics, University of Southampton, 1993.
¹⁴
Weinstein J, Kohn K, Grever M. Neural computing in cancer drug development: predicting mechanism of action. Science 1992;258:447-451.
¹⁵
Baxt WJ. Application of artificial neural network to clinical medicine. Lancet 1995;346:1135-1138.

Publication Dates

Publication in this collection
Jul-Sep 2007

This is an open-access article distributed under the terms of the Creative Commons Attribution License

[1] ¹
Duffy FH, Albert MS, Mcnulty G, Garvey AJ. Age differences in brain electrical activity of healthy subjects. Ann Neural 1984;16:430-438.

[2] ²
Nuwer MR, Comi G, Emerson R, et al. IFCN standards for digital recording of clinical EEG. Electroencephalogr Clin Neurophysiol 1998;106:259-261.

[3] ³
Nitrini R, Caramelli P, Bottino CM, Damasceno BP, Brucki SM, Anghinah R. Academia Brasileira de Neurologia. Diagnosis of Alzheimer's disease in Brazil: diagnostic criteria and auxiliary tests. Recommendations of the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology. Arq Neuropsiquiatr 2005;63:713-719.

[4] ⁴
Montenegro MA, Cendes F, Guerreiro MM, Guerreiro CAM, editors. EEG na Prática Clínica, São Paulo: Lemos Editorial; 2001.

[5] ⁵
Abe JM. Fundamentos da lógica anotada. Thesis, Faculdade Filosofia Letras e Ciências Humanas da Universidade de São Paulo, São Paulo, Brazil, 1992.

[6] ⁶
Anghinah R. Estudo da densidade espectral e da coerência do eletrencefalograma em indivíduos adultos normais e com doença de Alzheimer provável. Thesis, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil, 2003.

[7] ⁷
Da Costa NCA, Abe JM, Silva Filho JI, Murolo AC, Leite CFS. Lógica Paraconsistente Aplicada. São Paulo: Editora Atlas; 1999.

[8] ⁸
Da Silva Filho JI, Abe JM. Fundamentos das redes neurais paraconsistentes: destacando aplicações em neurocomputação. São Paulo: Editora Arte & Ciência; 2001.

[9] ⁹
Erganian A, Mahmoudi B. Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface. Med Biol Eng Comput 2005;43:296-305.

[10] ¹⁰
Fausett L. Fundamentals of neural network architectures, algorithms, and applications, US Ed edition, New York: Prentice Hall, 1994.

[11] ¹¹
Subasi A, Alkan A, Koklukaya E, Kiymik MK. Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing, Neural Netw 2005;18:985-997.

[12] ¹²
Ventouras EM, Monoyou EA, Ktonas PY, et al. 25 Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study. Comput Methods Programs Biomed 2005;78:191-207.

[13] ¹³
Miller A. The application of neural networks to imaging and signal processing in astronomy and medicine. Thesis, Faculty of Science, Department of Physics, University of Southampton, 1993.

[14] ¹⁴
Weinstein J, Kohn K, Grever M. Neural computing in cancer drug development: predicting mechanism of action. Science 1992;258:447-451.

[15] ¹⁵
Baxt WJ. Application of artificial neural network to clinical medicine. Lancet 1995;346:1135-1138.

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