Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease
Covariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here co...
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MDPI AG
2021-10-01
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author | Gabriela González-González Víctor M. Velasco-Herrera Alicia Ortega-Aguilar |
author_facet | Gabriela González-González Víctor M. Velasco-Herrera Alicia Ortega-Aguilar |
author_sort | Gabriela González-González |
collection | DOAJ |
description | Covariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here could be applied to any neurological condition, including the important early stages of neurodegenerative diseases. In this study, we analyzed EEG from control (CL) participants and participants with diagnosed Parkinson’s disease (PD), who were age-matched women in an eyes-closed resting state, to test the model. PD is predicted to rise over the next decades as the population ages. Furthermore, women are more likely to undergo PD-related complications and worse disability than men. Two groups based on age were considered: under and over 60 years (PD patients <60 and >60; CL <60 and >60). Continuous Wavelet Transform and Cross Wavelet Transform were applied to determine patterns of global wavelet curves, main frequencies, and power analyses. Our results indicate that both CL age groups and PD patients <60 share a main α brainwave and PD patients >60 showed a main δ brainwave. Interestingly, power anomalies analyses show a decreasing anteroposterior gradient in CL, whereas it is increasing in PD patients, which was not previously observed. The brainwave power in PD patients <60 was higher in θ, α and β waves and in >60 group, the δ, θ and β brainwaves were predominant. This methodology offers a tool to reveal abnormal electrical brain activity unseen by a regular EEG analysis. The advent of new models that process EEG, such as the model proposed in this study, promotes renewed interest in electrophysiology of the brain to study the early stages of PD and improve understanding of the origin and progress of the disease. |
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language | English |
last_indexed | 2024-03-10T06:44:29Z |
publishDate | 2021-10-01 |
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spelling | doaj.art-8f668e36502f412cb26fc470ffc7d99a2023-11-22T17:21:50ZengMDPI AGApplied Sciences2076-34172021-10-011120963310.3390/app11209633Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s DiseaseGabriela González-González0Víctor M. Velasco-Herrera1Alicia Ortega-Aguilar2Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, MexicoInstituto de Geofísica, Universidad Nacional Autónoma de México, Mexico City 04510, MexicoDepartamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, MexicoCovariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here could be applied to any neurological condition, including the important early stages of neurodegenerative diseases. In this study, we analyzed EEG from control (CL) participants and participants with diagnosed Parkinson’s disease (PD), who were age-matched women in an eyes-closed resting state, to test the model. PD is predicted to rise over the next decades as the population ages. Furthermore, women are more likely to undergo PD-related complications and worse disability than men. Two groups based on age were considered: under and over 60 years (PD patients <60 and >60; CL <60 and >60). Continuous Wavelet Transform and Cross Wavelet Transform were applied to determine patterns of global wavelet curves, main frequencies, and power analyses. Our results indicate that both CL age groups and PD patients <60 share a main α brainwave and PD patients >60 showed a main δ brainwave. Interestingly, power anomalies analyses show a decreasing anteroposterior gradient in CL, whereas it is increasing in PD patients, which was not previously observed. The brainwave power in PD patients <60 was higher in θ, α and β waves and in >60 group, the δ, θ and β brainwaves were predominant. This methodology offers a tool to reveal abnormal electrical brain activity unseen by a regular EEG analysis. The advent of new models that process EEG, such as the model proposed in this study, promotes renewed interest in electrophysiology of the brain to study the early stages of PD and improve understanding of the origin and progress of the disease.https://www.mdpi.com/2076-3417/11/20/9633covariancewavelet analysesParkinson’s diseaseelectroencephalographic record (EEG)non-motor symptoms |
spellingShingle | Gabriela González-González Víctor M. Velasco-Herrera Alicia Ortega-Aguilar Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease Applied Sciences covariance wavelet analyses Parkinson’s disease electroencephalographic record (EEG) non-motor symptoms |
title | Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease |
title_full | Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease |
title_fullStr | Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease |
title_full_unstemmed | Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease |
title_short | Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease |
title_sort | use of covariance analysis in electroencephalogram reveals abnormalities in parkinson s disease |
topic | covariance wavelet analyses Parkinson’s disease electroencephalographic record (EEG) non-motor symptoms |
url | https://www.mdpi.com/2076-3417/11/20/9633 |
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