TY - GEN
T1 - Investigating EEG Pattern during Designed-Hand Movement Tasks in Stroke Patients
AU - Novitasari, Made Dwi
AU - Wibawa, Adhi Dharma
AU - Purnomo, Mauridhi Hery
AU - Islamiyah, Wardah Rahmatul
AU - Fatoni, Ali
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - EEG Analysis
KW - Monitoring Stroke Rehabilitation
KW - Rehabilitation
KW - Stroke
KW - Time Domain Feature
UR - http://www.scopus.com/inward/record.url?scp=85091709517&partnerID=8YFLogxK
U2 - 10.1109/ISITIA49792.2020.9163680
DO - 10.1109/ISITIA49792.2020.9163680
M3 - Conference contribution
AN - SCOPUS:85091709517
T3 - Proceedings - 2020 International Seminar on Intelligent Technology and Its Application: Humanification of Reliable Intelligent Systems, ISITIA 2020
SP - 141
EP - 147
BT - Proceedings - 2020 International Seminar on Intelligent Technology and Its Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Seminar on Intelligent Technology and Its Application, ISITIA 2020
Y2 - 22 July 2020 through 23 July 2020
ER -