Citation

Lim, Choon Chen and Sim, Kok Swee and Tan, Shing Chiang (2021) Assessment of stroke severity level using EMG, EEG and ECG for virtual rehabilitation. Engineering Letters, 29 (2). pp. 1-14. ISSN 2193-567X

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Abstract

Stroke is a popular disease that brings concern to medical experts around the world. Thus, assessment of the stroke severity level is essential to make sure that the physiotherapists can assign the patients to suitable rehabilitation training. In this paper, stroke severity level of the stroke patients can be determined by using the combination of electromyogram (EMG), electroencephalogram (EEG) and electrocardiogram (ECG) measurements. The EMG, EEG and ECG signals will go through signal process to eliminate the unnecessary features of the signal. The power spectrums and frequencies of each biosignal are computed. The calculated outcome will be inserted into a matrix equation to compute a stroke vector (s) for determining the severity level of the patient. The stroke vector will be used to assign the patient with the appropriate virtual rehabilitation training. Two virtual rehabilitation training, which are ‘Pick & Place’ and ‘Stone Breaker’, are included in a program for stroke rehabilitation. This virtual rehabilitation program is useful to assist in the restoration of upper limb motor and fingers motor of stroke patients. A total of 40 stroke patients had joined the research study. 30 of them were recruited to join the biosignals testing for the formation of stroke vector equation using machine learning. Another 10 patients were assigned for the result testing of the developed system. The result had shown that the designed framework could effectively assign the patients to suitable virtual rehabilitation training through the stroke vector calculation. Besides, the stroke assessment system had achieved a high accuracy of 96.67% in determining the stroke severity level of stroke patients. By using the computerized stroke assessment method, it enhances the accuracy in the assessment of stroke severity level.

Item Type:	Article
Uncontrolled Keywords:	Virtual computer systems, EMG, EEG, ECG, Stroke Severity Level, Virtual Rehabilitation,
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	30 Jun 2021 03:30
Last Modified:	30 Jun 2021 03:30
URII:	http://shdl.mmu.edu.my/id/eprint/8880

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