Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis

Article type: Research Article

Authors: Lazar, Prinza^a | Jayapathy, Rajeesh^{b; c} | Torrents-Barrena, Jordina^c; | Mary Linda, M.^d | Mol, Beena^e | Mohanalin, J.^f | Puig, Domenec^c

Affiliations: [a] Department of Electronics and Communication, TKR College of Engineering and Technology, Hyderabad, 500097, India | [b] Department of Electronics and Communication Engineering, College of Engineering, Thalassery, 670107, India | [c] Department of Computer Engineering and Mathematics, University Rovira i Virgili, Tarragona, 43007, Spain | [d] Department of Electrical and Electronics Engineering, Ponjesly College of Engineering, Nagercoil, 629003, India | [e] Department of Civil Engineering, LBS College of Engineering, Kasaragod, 671542, India | [f] Department of Electrical and Electronics Engineering, College of Engineering Pathanpuram, Pathanpuram, 689696, India

Correspondence: [*] Corresponding author.  E-mail: jordina.torrents@urv.cat

Abstract: Alzheimer is a degenerative disorder that attacks neurons, resulting in loss of memory, thinking, language skills, and behavioral changes. Computer-aided detection methods can uncover crucial information recorded by electroencephalograms. A systematic literature search presents the wavelet transform as a frequently used technique in Alzheimer’s detection. However, it requires a defined basis function considered a significant problem. In this work, the concept of empirical mode decomposition is introduced as an alternative to process Alzheimer signals. The performance of empirical mode decomposition heavily relies on a parameter called threshold. In our previous works, we found that the existing thresholding techniques were not able to highlight relevant information. The use of Tsallis entropy as a thresholder is evaluated through the combination of empirical mode decomposition and neural networks. Thanks to the extraction of better features that boost the classification accuracy, the proposed approach outperforms the state-of-the-art in terms of peak signal to noise ratio and root mean square error. Hence, our methodology is more likely to succeed than methods based on other landmarks such as Bayes, Normal and Visu shrink. We finally report an accuracy rate of 80%, while the aforementioned techniques only yield performances of 65%, 60% and 40%, respectively.

Keywords: Alzheimer’s disease, EEG signals, empirical mode decomposition, neural network, Tsallis entropy

DOI: 10.3233/BME-181008

Journal: Bio-Medical Materials and Engineering, vol. 29, no. 5, pp. 551-566, 2018