Application of Hjorth parameters in the classification of healthy aging EEG signals
Aging has extensive impacts on brain cognition. In this work we proposed a method using Hjorth parameters to classify the elderly’s electroencephalography (EEG) signals from that of middle age group by applying K-nearest neighbor (KNN) and Random forest (RF) classifiers. We acquired EEG of 20 heal...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Prince of Songkla University
2021-12-01
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| Series: | Songklanakarin Journal of Science and Technology (SJST) |
| Subjects: | |
| Online Access: | https://rdo.psu.ac.th/sjst/journal/43-6/37.pdf |
| _version_ | 1818208194447540224 |
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| author | Hamad Javaid Krit Charupanit Ekkasit Kumarnsit Surapong Chatpun |
| author_facet | Hamad Javaid Krit Charupanit Ekkasit Kumarnsit Surapong Chatpun |
| author_sort | Hamad Javaid |
| collection | DOAJ |
| description | Aging has extensive impacts on brain cognition. In this work we proposed a method using Hjorth parameters to classify
the elderly’s electroencephalography (EEG) signals from that of middle age group by applying K-nearest neighbor (KNN) and
Random forest (RF) classifiers. We acquired EEG of 20 healthy middle age subjects and 20 healthy elderly subjects in resting
state eyes-open for 5 minutes and eyes-closed for 5 minutes using an 8-electrodes device. Euclidean and Manhattan distance
measures were tested using KNN. The classifier performance was evaluated by using accuracy, sensitivity, specificity, and kappa
statistic. The best accuracy achieved was 91.25 %, and kappa statistic of 0.825, in eyes-closed state. In eyes-open state 90%
accuracy was achieved with kappa statistic of 0.80. RF achieved 83.75% accuracy with kappa statistic of 0.675 in eyes-closed
state and 78.75% accuracy with Kappa statistic of 0.575 in eyes-open state. The KNN performed better using Manhattan distance
function in both eyes-open and eyes-closed states. Results showed the potential of Hjorth parameters as the suitable EEG features
in the classification of EEG aging signals.
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| first_indexed | 2024-12-12T04:40:56Z |
| format | Article |
| id | doaj.art-8c40527f7e9b4ca69bf41256ccf7d0bb |
| institution | Directory Open Access Journal |
| issn | 0125-3395 |
| language | English |
| last_indexed | 2024-12-12T04:40:56Z |
| publishDate | 2021-12-01 |
| publisher | Prince of Songkla University |
| record_format | Article |
| series | Songklanakarin Journal of Science and Technology (SJST) |
| spelling | doaj.art-8c40527f7e9b4ca69bf41256ccf7d0bb2022-12-22T00:37:49ZengPrince of Songkla UniversitySongklanakarin Journal of Science and Technology (SJST)0125-33952021-12-014361807181410.14456/sjst-psu.2021.237Application of Hjorth parameters in the classification of healthy aging EEG signalsHamad Javaid0Krit Charupanit1Ekkasit Kumarnsit2Surapong Chatpun3Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110 ThailandDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110 ThailandPhysiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, 90112 ThailandDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110 ThailandAging has extensive impacts on brain cognition. In this work we proposed a method using Hjorth parameters to classify the elderly’s electroencephalography (EEG) signals from that of middle age group by applying K-nearest neighbor (KNN) and Random forest (RF) classifiers. We acquired EEG of 20 healthy middle age subjects and 20 healthy elderly subjects in resting state eyes-open for 5 minutes and eyes-closed for 5 minutes using an 8-electrodes device. Euclidean and Manhattan distance measures were tested using KNN. The classifier performance was evaluated by using accuracy, sensitivity, specificity, and kappa statistic. The best accuracy achieved was 91.25 %, and kappa statistic of 0.825, in eyes-closed state. In eyes-open state 90% accuracy was achieved with kappa statistic of 0.80. RF achieved 83.75% accuracy with kappa statistic of 0.675 in eyes-closed state and 78.75% accuracy with Kappa statistic of 0.575 in eyes-open state. The KNN performed better using Manhattan distance function in both eyes-open and eyes-closed states. Results showed the potential of Hjorth parameters as the suitable EEG features in the classification of EEG aging signals. https://rdo.psu.ac.th/sjst/journal/43-6/37.pdfelectroencephalographyaginghjorth parametersk-nearest neighborclassification |
| spellingShingle | Hamad Javaid Krit Charupanit Ekkasit Kumarnsit Surapong Chatpun Application of Hjorth parameters in the classification of healthy aging EEG signals Songklanakarin Journal of Science and Technology (SJST) electroencephalography aging hjorth parameters k-nearest neighbor classification |
| title | Application of Hjorth parameters in the classification of healthy aging EEG signals |
| title_full | Application of Hjorth parameters in the classification of healthy aging EEG signals |
| title_fullStr | Application of Hjorth parameters in the classification of healthy aging EEG signals |
| title_full_unstemmed | Application of Hjorth parameters in the classification of healthy aging EEG signals |
| title_short | Application of Hjorth parameters in the classification of healthy aging EEG signals |
| title_sort | application of hjorth parameters in the classification of healthy aging eeg signals |
| topic | electroencephalography aging hjorth parameters k-nearest neighbor classification |
| url | https://rdo.psu.ac.th/sjst/journal/43-6/37.pdf |
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