Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease
Alzheimer’s disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD. To this end, we propose an extension of multiscale entropy based on variance (MSEσ2) to multich...
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| Format: | Article |
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MDPI AG
2017-01-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/19/1/31 |
| _version_ | 1798041565179936768 |
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| author | Hamed Azami Daniel Abásolo Samantha Simons Javier Escudero |
| author_facet | Hamed Azami Daniel Abásolo Samantha Simons Javier Escudero |
| author_sort | Hamed Azami |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD. To this end, we propose an extension of multiscale entropy based on variance (MSEσ2) to multichannel signals, termed multivariate MSEσ2 (mvMSEσ2), to take into account both the spatial and time domains of time series. Then, we investigate the mvMSEσ2 of EEGs at different frequency bands, including the broadband signals filtered between 1 and 40 Hz, θ, α, and β bands, and compare it with the previously-proposed multiscale entropy based on mean (MSEµ), multivariate MSEµ (mvMSEµ), and MSEσ2, to distinguish different kinds of dynamical properties of the spread and the mean in the signals. Results from 11 AD patients and 11 age-matched controls suggest that the presence of broadband activity of EEGs is required for a proper evaluation of complexity. MSEσ2 and mvMSEσ2 results, showing a loss of complexity in AD signals, led to smaller p-values in comparison with MSEµ and mvMSEµ ones, suggesting that the variance-based MSE and mvMSE can characterise changes in EEGs as a result of AD in a more detailed way. The p-values for the slope values of the mvMSE curves were smaller than for MSE at large scale factors, also showing the possible usefulness of multivariate techniques. |
| first_indexed | 2024-04-11T22:23:20Z |
| format | Article |
| id | doaj.art-f1e657c1006c4237a51adcbc2456f9f4 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T22:23:20Z |
| publishDate | 2017-01-01 |
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| spelling | doaj.art-f1e657c1006c4237a51adcbc2456f9f42022-12-22T03:59:59ZengMDPI AGEntropy1099-43002017-01-011913110.3390/e19010031e19010031Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s DiseaseHamed Azami0Daniel Abásolo1Samantha Simons2Javier Escudero3Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UKCentre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UKCentre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UKInstitute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UKAlzheimer’s disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD. To this end, we propose an extension of multiscale entropy based on variance (MSEσ2) to multichannel signals, termed multivariate MSEσ2 (mvMSEσ2), to take into account both the spatial and time domains of time series. Then, we investigate the mvMSEσ2 of EEGs at different frequency bands, including the broadband signals filtered between 1 and 40 Hz, θ, α, and β bands, and compare it with the previously-proposed multiscale entropy based on mean (MSEµ), multivariate MSEµ (mvMSEµ), and MSEσ2, to distinguish different kinds of dynamical properties of the spread and the mean in the signals. Results from 11 AD patients and 11 age-matched controls suggest that the presence of broadband activity of EEGs is required for a proper evaluation of complexity. MSEσ2 and mvMSEσ2 results, showing a loss of complexity in AD signals, led to smaller p-values in comparison with MSEµ and mvMSEµ ones, suggesting that the variance-based MSE and mvMSE can characterise changes in EEGs as a result of AD in a more detailed way. The p-values for the slope values of the mvMSE curves were smaller than for MSE at large scale factors, also showing the possible usefulness of multivariate techniques.http://www.mdpi.com/1099-4300/19/1/31Alzheimer’s diseasecomplexitymultivariate generalized multiscale entropystatistical momentselectroencephalogram |
| spellingShingle | Hamed Azami Daniel Abásolo Samantha Simons Javier Escudero Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease Entropy Alzheimer’s disease complexity multivariate generalized multiscale entropy statistical moments electroencephalogram |
| title | Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease |
| title_full | Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease |
| title_fullStr | Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease |
| title_full_unstemmed | Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease |
| title_short | Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease |
| title_sort | univariate and multivariate generalized multiscale entropy to characterise eeg signals in alzheimer s disease |
| topic | Alzheimer’s disease complexity multivariate generalized multiscale entropy statistical moments electroencephalogram |
| url | http://www.mdpi.com/1099-4300/19/1/31 |
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