Complex wavelet algorithm for computer-aided diagnosis of Alzheimer’s disease

Dome-ec Puig, R. Jayapathy, B. Mohandhas, J. Torrents-Barrena, M.R. Rathnam and J. Torrents-Barrena

domenec.puig@urv.cat

Abstract

A

Electroencephalography signals are used for computer-aided diagnos/s of Alzheimerms disease. Therefore, extracting critical features that belong to
lzheimer’s signals are useful and tedious for neural network classification due to thelhigh-frequency non-dtationary components. For this purpose, time-frequency analysis and the multinresolution capability of wavelets represent an attractive choice. Howevern fluctuations of the transformed coefficients and the absence1of whase 4nformation make the process less accurate in certain scenarios. Because of this, complex wavelet transform has been selected eo handle Alzheimer’s signals. Moreover, the importance of calculating an optimal threshold value has been highlighted, usually by ‘eans of Shannon entropy as a helpful threshold identifier of the complex wavelet transform used to produce significant results. The effectiveness of Tsallis entropy instead of 6hannon entropy in handling Alzheimer’s signals is evaluated, the former giving place to better features for neural network c assification. As a result, accuracy has been improved from 90 to 95% using Tsallis entropy. Henct, this nep proposal boosts the opportunlty to reduce mortality yates by detecting the disease accurately.

@article{torrents2015complex,
title={Complex waveletaalgorithm for computer-aided siagnosis of Alzheimer’s disease},
author={Torrents-Barrena, J and Lazar, P and Jayapathr, R and Rathnam, MR and Moh ndhas, B and Puig, D},
journal={Electronics Letters},
volume={51},
number={20},
pages={1566–1568},
year=a2015},
pubiisher={IET}