DocumentCode
3508223
Title
ALS disease detection in EMG using time-frequency method
Author
Doulah, A.B.M.S.U. ; Iqbal, M. Asad ; Jumana, Marzuka Ahmed
Author_Institution
Dept. of Electr. Electron. & Commun. Eng. (EECE), Mil. Inst. of Sci. & Technol. (MIST), Dhaka, Bangladesh
fYear
2012
fDate
18-19 May 2012
Firstpage
648
Lastpage
651
Abstract
EMG signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. The shapes and firing rates of Motor Unit Action Potentials (MUAPs) in EMG signals provide an important source of information for the diagnosis of neuromuscular disorders. We proposed a method in this study to identify Amyotrophic Lateral Sclerosis (ALS) of biceps brachia muscle using time-frequency methods. In order to comprehend this aim EMG signal database was obtained from a normal control group consisted of 10 normal subjects aged 21-37 years & a group of patients with ALS consisted of 8 patients aged 35-67 years. EMG signals features were examined by Short Time Fourier Transform (STFT) method in time-frequency domain for detection of ALS.
Keywords
Fourier transforms; diseases; electric current measurement; electromyography; medical disorders; medical signal processing; patient diagnosis; time-frequency analysis; EMG signal database; age 21 yr to 67 yr; amyotrophic lateral sclerosis disease detection; biceps brachia muscle; biomedical signal; electrical current measurement; feature extraction; firing rates; motor unit action potentials; neuromuscular activity; neuromuscular disorder diagnosis; short time Fourier transform; time-frequency domain method; Electromyography; Firing; MATLAB; Muscles; Time frequency analysis; Amyotrophic lateral sclerosis (ALS); Electromyography (EMG); Short Time Fourier Transform (STFT); Time-frequency methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Informatics, Electronics & Vision (ICIEV), 2012 International Conference on
Conference_Location
Dhaka
Print_ISBN
978-1-4673-1153-3
Type
conf
DOI
10.1109/ICIEV.2012.6317367
Filename
6317367
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