Abstract

Stroke is a catastrophic disease with the second-highest mortality rate in the world. It is also the leading cause of disability in many countries. A stroke rehabilitation program is crucial for the recovery process of post-stroke patients. It must be supported by measurable monitoring. Rehabilitation monitoring is currently still carried out through visual and manual observation, so the measurement results have not been well presented and subjective. Monitoring using EEG can provide solutions to these needs. During the monitoring process, significant parameters of EEG need to be explored. This study aims to find the most stable parameters that could be applied as a basis for measuring progress in stroke rehabilitation monitoring. The parameters are searched by calculating the difference between the value of the features of healthy hand movements with affected hand movements in the same individual stroke patients. The hypothesis in this study is that the difference between the healthy hand and the affected hand in stroke patients is positive because the healthy side movement has a higher amplitude value than the affected side movement. The data in this study is obtained from EEG of 10 stroke patients during a designed task motion on C3 and C4 channels. Participants performed three movements, namely shoulder flexion-extension, elbow flexion-extension, and grasping. Motions are carried out on both sides of the hand, both the healthy and the affected side. For preprocessing the EEG, this study applies IIR at the bandpass filter stages. Followed by ASR and ICA algorithm to remove the artifact. The clean EEG is segmented into 20 ms before calculating the Mean, Mav, and STD features. The difference between the healthy side feature (HFV) and the stroke side feature (AFV) then will be calculated and analyzed. The results show that STD, during shoulder movements, and in low alpha frequencies is the best feature with the most positive HFV and AFV differences. From this study, it can be concluded that the STD feature, during shoulder movements, and in low alpha frequency band showed a high potential to be used as a crucial parameter to monitor the stroke rehabilitation progress.

Original languageEnglish
Title of host publicationProceedings - 2020 International Seminar on Intelligent Technology and Its Application
Subtitle of host publicationHumanification of Reliable Intelligent Systems, ISITIA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages141-147
Number of pages7
ISBN (Electronic)9781728174136
DOIs
Publication statusPublished - Jul 2020
Event2020 International Seminar on Intelligent Technology and Its Application, ISITIA 2020 - Virtual, Online, Indonesia
Duration: 22 Jul 202023 Jul 2020

Publication series

NameProceedings - 2020 International Seminar on Intelligent Technology and Its Application: Humanification of Reliable Intelligent Systems, ISITIA 2020

Conference

Conference2020 International Seminar on Intelligent Technology and Its Application, ISITIA 2020
Country/TerritoryIndonesia
CityVirtual, Online
Period22/07/2023/07/20

Keywords

  • EEG Analysis
  • Monitoring Stroke Rehabilitation
  • Rehabilitation
  • Stroke
  • Time Domain Feature

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