TY  - CONF
Y1  - 2015///
SN  - 18770509
PB  - Elsevier B.V.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962834084&doi=10.1016%2fj.procs.2015.12.346&partnerID=40&md5=d342f3b67f100cc89c746d2fb4f9bb44
A1  - Elamvazuthi, I.
A1  - Duy, N.H.X.
A1  - Ali, Z.
A1  - Su, S.W.
A1  - Khan, M.K.A.A.
A1  - Parasuraman, S.
VL  - 76
EP  - 228
AV  - none
N2  - Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification.
N1  - cited By 52; Conference of IEEE International Symposium on Robotics and Intelligent Sensors, IEEE IRIS 2015 ; Conference Date: 18 October 2015 Through 20 October 2015; Conference Code:123218
KW  - Classification (of information); Electromyography; Extraction; Feature extraction; Intelligent control; Multilayers; Neural networks; Neuromuscular rehabilitation; Neurophysiology; Robotics; Signal processing; Smart sensors
KW  -  Absolute values; Autoregressive methods; Multi layer perceptron; Neuromuscular disease; Root Mean Square; Zero-crossings
KW  -  Biomedical signal processing
TI  - Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron
SP  - 223
ID  - scholars6136
ER  -