![](bg6.png)
Hort, J., O’Brien, J. T., Gainotti, G., Pirttila, T., Popescu,
B. O., Rektorova, I., Sorbi, S., and Scheltens, P. (2010).
Efns guidelines for the diagnosis and management of
alzheimer’s disease. European Journal of Neurology,
17:1236–1248.
Jeong, J. (2004). Eeg dynamics in patients with alzheimer’s
disease. Clin. Neurophysiol, 115:1490–1505.
Marquardt, D. (1963). An algorithm for least-squares es-
timation of nonlinear parameters. SIAM Journal on
Applied Mathematics, 11:431–441.
Melissant, C., Ypma, A., Frietman, E., and Stam, C. (2005).
A method for detection of alzheimer’s disease using ica-
enhanced eeg measurements. Artif Intell Med, 33:209–
222.
Moreira, P. and Oliveira, C. (2005). A Doen
c¸
a de Alzheimer e
outras Dem
ˆ
encias em Portugal, chapter Fisiopatologia
da doen
c¸
a de Alzheimer e de outras dem
ˆ
encias., pages
41–60. Lisboa: Lidel Edic¸
˜
oes T
´
ecnicas.
Riedmiller, M. and Braun, H. (1993). A direct adaptive
method for faster backpropagation learning: The rprop
algorithm. Proceedings of the IEEE International Con-
ference on Neural Networks, 1:586–591.
Rioul, O. and Vetterli, M. (1992). Wavelets and signal pro-
cessing. IEEE Signal Processing Magazine, 8:14–38.
Rodrigues, P. (2011). Diagn
´
ostico da doen
c¸
a de alzheimer
com base no electroencefalograma. Master’s thesis,
Instituto Polit
´
enico de Bragan
c¸
a - Escola Superior de
Tecnologia e Gest
˜
ao.
Rodrigues, P. and Teixeira, J. (2011). Artificial neural net-
works in the discrimination of alzheimer’s disease.
Communications in Computer and Information Sci-
ence, 221:272–281.
Rodrigues, P., Teixeira, J., Hornero, R., Poza, J., and Car-
reres, A. (2011). Classification of alzheimer’s elec-
troencephalograms using artificial neural networks and
logistic regression. Japan - Portugal Nano-Biomedical
Engineering Symposium 2011, 1(ISBN-4-904157-20-
6):33–34.
Stahl, S. (2008). Stahl’s Essential Psychopharmacology.
Neuroscientific Basis and Practical Applications. Cam-
bridge University Press, third edition.
Vialatte, F., Cichocki, A., Dreyfus, G., Musha, T., Rutkowski,
T., and Gervais, R. (2005a). Blind early detection of
alzheimer’s disease by blind source separation and
bump modelling of eeg signals. Lectues Notes in Com-
puter Science, 3596:683–692.
Vialatte, F., Cichocki, A., Dreyfus, G., Musha, T., Shishkin,
S., and Gervais, R. (2005b). Early detection of
alzheimer’s disease by blind source separation, time
frequency representation, and bump modeling of eeg
signals. Lecture Notes in Computer Science, 3696:683–
692.
Vialatte, F., Maurice, M., and Cichocki, A. (2008). Why
sparse bump models? Neuroimage, 41:159.
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