NDLI: Complex wavelet algorithm for computer-aided diagnosis of Alzheimer's disease

Content Provider	IET Digital Library
Author	Barrena, J. Torrents Lazar, P. Jayapathy, R. Rathnam, M. R. Mohandhas, B. Puig, D.
Abstract	Electroencephalography signals are used for computer-aided diagnosis of Alzheimer's disease. Therefore, extracting critical features that belong to Alzheimer's signals are useful and tedious for neural network classification due to the high-frequency non-stationary components. For this purpose, time–frequency analysis and the multi-resolution capability of wavelets represent an attractive choice. However, fluctuations of the transformed coefficients and the absence of phase information make the process less accurate in certain scenarios. Because of this, complex wavelet transform has been selected to handle Alzheimer's signals. Moreover, the importance of calculating an optimal threshold value has been highlighted, usually by means 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 Shannon entropy in handling Alzheimer's signals is evaluated, the former giving place to better features for neural network classification. As a result, accuracy has been improved from 90 to 95% using Tsallis entropy. Hence, this new proposal boosts the opportunity to reduce mortality rates by detecting the disease accurately.
Starting Page	1566
Ending Page	1568
Page Count	3
ISSN	00135194
Volume Number	51
e-ISSN	1350911X
Issue Number	Issue 20, Oct (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/51/20
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2015.1735
Journal	Electronics Letters
Publisher Date	2015-09-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Alzheimer Disease Bioelectric Signal Biology And Medical Computing Complex Wavelet Algorithm Computer-aided Diagnosis Digital Signal Processing Diseases Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalography Electroencephalography Signal Function Theory And Analysis Integral Transforms Mathematical Analysis Medical Signal Processing Multiresolution Capability Neural Computing Technique Neural Network Classification Signal Classification Signal Processing And Detection Time-frequency Analysis Time–frequency Analysis Tsallis Entropy Wavelet Neural Nets Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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