Fuzzy-entropy threshold based on a complex wavelet denoising technique to diagnose Alzheimer disease

Healthcare Technology Letters
Prinza LazarDomenec Puig

Abstract

The presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer's disease (AD) diagnosis. In addition, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis through an optimum threshold will likely achieve better results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has been proposed to obtain the most appropriate threshold. First, the complex coefficients are fuzzified using a Gaussian membership function. Afterwards, the ability of the proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that the authors' methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several features to classify AD from normal EEG signals obtaining a specificity of 87.5%.

References

Feb 22, 2005·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Andrzej CichockiTakayoshi Kurachi
Feb 12, 2011·Journal of Neuroscience Methods·Ziad Sankari, Hojjat Adeli
Jun 2, 2011·International Journal of Alzheimer's Disease·Roberta LizioClaudio Babiloni
Oct 1, 2011·International Journal of Neural Systems·Fang HanZhijie Wang
May 1, 2013·International Journal of Neural Systems·U Rajendra AcharyaChoo Min Lim
Nov 27, 2015·Healthcare Technology Letters·Samantha SimonsJavier Escudero

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