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Application of Hjorth parameters in the classificationof healthy aging EEG signals |
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| รหัสดีโอไอ | |
| Creator | 1. Hamad Javaid 2. Krit Charupanit 3. Ekkasit Kumarnsit 4. Surapong Chatpun |
| Title | Application of Hjorth parameters in the classificationof healthy aging EEG signals |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2564 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 43 |
| Journal No. | 6 |
| Page no. | 1807-1814 |
| Keyword | electroencephalography, aging, Hjorth parameters, k-nearest neighbor, classification |
| URL Website | https://rdo.psu.ac.th/sjst/index.php |
| ISSN | 0125-3395 |
| Abstract | Aging has extensive impacts on brain cognition. In this work we proposed a method using Hjorth parameters to classifythe elderlyโs electroencephalography (EEG) signals from that of middle age group by applying K-nearest neighbor (KNN) andRandom forest (RF) classifiers. We acquired EEG of 20 healthy middle age subjects and 20 healthy elderly subjects in restingstate eyes-open for 5 minutes and eyes-closed for 5 minutes using an 8-electrodes device. Euclidean and Manhattan distancemeasures were tested using KNN. The classifier performance was evaluated by using accuracy, sensitivity, specificity, and kappastatistic. 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-closedstate and 78.75% accuracy with Kappa statistic of 0.575 in eyes-open state. The KNN performed better using Manhattan distancefunction in both eyes-open and eyes-closed states. Results showed the potential of Hjorth parameters as the suitable EEG featuresin the classification of EEG aging signals. |
