Songklanakarin Journal of Science and Technology (SJST) (Dec 2021)

Application of Hjorth parameters in the classification of healthy aging EEG signals

  • Hamad Javaid,
  • Krit Charupanit,
  • Ekkasit Kumarnsit,
  • Surapong Chatpun

DOI
https://doi.org/10.14456/sjst-psu.2021.237
Journal volume & issue
Vol. 43, no. 6
pp. 1807 – 1814

Abstract

Read online

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.

Keywords