Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis

Author:

Cataldo Andrea1ORCID,Criscuolo Sabatina2ORCID,De Benedetto Egidio De2ORCID,Masciullo Antonio1ORCID,Pesola Marisa2ORCID,Schiavoni Raissa1ORCID

Affiliation:

1. Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy

2. Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy

Abstract

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer’s disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigation.

Publisher

MDPI AG

Subject

Bioengineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Entropy and Coherence Features in EEG-Based Classification for Alzheimer's Disease Detection;2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC);2024-05-20

2. A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy;Bioengineering;2024-03-27

3. EEG complexity-based algorithm using Multiscale Fuzzy Entropy: Towards a detection of Alzheimer’s disease;Measurement;2024-02

4. Entropy-Based EEG Measures for Revealing Altered Neural Dynamics in Alzheimer's Disease: A Preliminary Study;2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE);2023-10-25

5. Exploring the Relationship Between Performance and Environmental Sustainability in Measurement Systems: A Preliminary Study;2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE);2023-10-25

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