DWT based Identification of Amyotrophic Lateral Sclerosis Using Surface EMG Signal

Archana Bhaskarrao Sonone

Authors

Archana Bhaskarrao Sonone

Keywords:

ALS, sEMG, ZCR, RMS, MF, WL, DWT

Abstract

In the process of identification of Amyotrophic Lateral Sclerosis (ALS) which is a motor neuron disorder, extraction of feature is the most important step. In this work normal and ALS class for identification and monitoring have been included. Analysis of surface electromyography (sEMG) signal for ALS identification using discrete wavelet transform is most simple and powerful method being used all over the world. Time domain parameters, like Zero Crossing Rate (ZCR) and Root Mean Square (RMS) and frequency domain parameters like Mean Frequency (MF) and Waveform Length (WL) are considered. Threshold values for the above mentioned parameters are calculated for both the normal and ALS classes. Discrete Wavelet Transform (DWT) parameters are considered and their threshold values are also calculated for both normal and ALS classes. Surface EMG (sEMG) signal database of normal and ALS patients for both male and female is considered.

Downloads

How to Cite

DWT based Identification of Amyotrophic Lateral Sclerosis Using Surface EMG Signal. (2017). Global Journal of Medical Research, 17(F2), 1-5. https://medicalresearchjournal.org/index.php/GJMR/article/view/1379

Download Citation

References

A Shaikh Anowarul Fattah, Md Sayeed Ud Doulah, Celia Iqbal, Wei-Ping Shahnaz, M Zhu, Ahmad (2013) Identification of Motor Neuron Disease Using Wavelet Domain Features Extracted from EMG Signal.

A Sayeed Ud Doulaht, Md Asifiqbal, Ahmed Marzuka, Jumana (2012) ALS Disease Detection in EMG Using Time Frequency method.

V Amol Lolure, Thool (2012) Wavelet Transform Based EMG Feature Extraction and Evaluation Using Scatter Graphs.

Hossein Parsae, J Mehrdad, Daniel Gangeh, Mohamed Stashuk, Kamel (2012) Augmenting the Decomposition of EMG Signals Using Supervised Feature Extraction Techniques.

P Pal, N Mohanty, A Kushwaha, B Singh, B Mazumdar, T Gandhi (2010) Feature extraction for evaluation of Muscular Atrophy. 1-4.

I Elamvazuthi, G Ling, K Nurhanim, P Vasant, S Parasuraman (2013) Surface electromyography (sEMG) feature extraction based on Daubechies wavelets. 1492-1495.

Sofia Ben, Jebara (2013) Extraction of EMG Signal Buried in RMI Noise.

A Sumit, Raurale Acquisition and Processing Realtime EMG signals for Prosthesis Active Hand Movements.

A Sayeed Ud Doulaht, Md Asifiqbal, Ahmed Marzuka, Jumana (2012) ALS Disease Detection in EMG Using Time Frequency Method.

Md, Muhammad Rezwanul Ahsan, Ibn Ibrahimy, Othman Omran, Khalifa (2012) Optimization of Neural Network for Efficient EMG Signal Classification.

H Ali, Guido Al-Timemy, Javier Bugmann, Nicholas Escudero, Outram Classification of Finger 12. Movements for the Dexterous Hand Prosthesis Control With surface electromyography. 17(3).