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Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain

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Abstract

Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–11 Hz), alpha 2 (11–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz), and gamma (30–45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive–specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.

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Data Availability

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

This work was partially supported by the Italian Ministry of Health for Institutional Research (Ricerca corrente) and by Toto Holding.

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Authors and Affiliations

  1. Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy

    Chiara Pappalettera, Alessia Cacciotti, Lorenzo Nucci, Francesca Miraglia, Paolo Maria Rossini & Fabrizio Vecchio

  2. Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy

    Chiara Pappalettera, Alessia Cacciotti, Francesca Miraglia & Fabrizio Vecchio

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  1. Chiara Pappalettera
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  2. Alessia Cacciotti
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  3. Lorenzo Nucci
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Correspondence to Fabrizio Vecchio.

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Pappalettera, C., Cacciotti, A., Nucci, L. et al. Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain. GeroScience 45, 1131–1145 (2023). https://doi.org/10.1007/s11357-022-00710-4

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