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Article

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

1
Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
2
Laboratorio Universidad Politécnica de Madrid-Universidad Complutense de Madrid (UPM-UCM) de Neurociencia Cognitiva y Computacional, Departamento de Psiquiatría y Psicología Médica, Universidad Complutense de Madrid, Madrid 28040, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(4), 141; https://doi.org/10.3390/e19040141
Received: 30 January 2017 / Revised: 14 March 2017 / Accepted: 22 March 2017 / Published: 25 March 2017
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications)
The characterisation of healthy ageing of the brain could help create a fingerprint of normal ageing that might assist in the early diagnosis of neurodegenerative conditions. This study examined changes in resting state magnetoencephalogram (MEG) permutation entropy due to age and gender in a sample of 220 healthy participants (98 males and 122 females, ages ranging between 7 and 84). Entropy was quantified using normalised permutation entropy and modified permutation entropy, with an embedding dimension of 5 and a lag of 1 as the input parameters for both algorithms. Effects of age were observed over the five regions of the brain, i.e., anterior, central, posterior, and left and right lateral, with the anterior and central regions containing the highest permutation entropy. Statistically significant differences due to age were observed in the different brain regions for both genders, with the evolutions described using the fitting of polynomial regressions. Nevertheless, no significant differences between the genders were observed across all ages. These results suggest that the evolution of entropy in the background brain activity, quantified with permutation entropy algorithms, might be considered an alternative illustration of a ‘nominal’ physiological rhythm. View Full-Text
Keywords: permutation entropy; modified permutation entropy; magnetoencephalogram; ageing permutation entropy; modified permutation entropy; magnetoencephalogram; ageing
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MDPI and ACS Style

Shumbayawonda, E.; Fernández, A.; Hughes, M.P.; Abásolo, D. Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing. Entropy 2017, 19, 141. https://doi.org/10.3390/e19040141

AMA Style

Shumbayawonda E, Fernández A, Hughes MP, Abásolo D. Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing. Entropy. 2017; 19(4):141. https://doi.org/10.3390/e19040141

Chicago/Turabian Style

Shumbayawonda, Elizabeth, Alberto Fernández, Michael P. Hughes, and Daniel Abásolo. 2017. "Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing" Entropy 19, no. 4: 141. https://doi.org/10.3390/e19040141

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