Title :
Discrete wavelet transform and k-nn classification in EMG signals for diagnosis of neuromuscular disorders
Author :
Gallego Duque, Carlos Julian ; Duque Munoz, Leonardo ; Grajales Mejia, Jeisson ; Delgado Trejos, Edilson
Author_Institution :
Res. Group: Autom., Electron. y Cienc. Computacionales, MIRP Inst. Tecnol. Metropolitano Medellin, Medellin, Colombia
Abstract :
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles, during voluntary or involuntary muscle activities. An electromyography detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. The signals can be analyzed to detect medical abnormalities in the muscles. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. An accurate and computationally efficient means of classifying electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. The analysis of EMG signals provides an important source of information for the diagnosis of neuromuscular disorders. Different methodologies in the time domain and frequency domain have been followed for the analysis of EMG signals. In this study, the usefulness of the Discrete Wavelet Transform, besides, statistical features are presented for the classification of intramuscular EMG signals. The proposed method automatically classifies the EMG signals into normal, Amyotrophic lateral sclerosis, or myopathy. The k-nn classifier is used to give the recognition result. The experimental results show the high accuracy of the proposed method.
Keywords :
discrete wavelet transforms; electromyography; medical disorders; medical signal processing; neurophysiology; signal classification; statistical analysis; K-nn classification; amyotrophic lateral sclerosis; anatomical properties; biomedical signal; discrete wavelet transform; electrical activity; electrical potential; electromyographic signal pattern classification; frequency domain; intramuscular EMG signal classification; involuntary muscle activity; medical abnormality detection; muscle cells; myopathy; neuromuscular disorder diagnosis; noisy environment; physiological properties; skeletal muscles; statistical features; time domain; voluntary muscle activity; Discrete wavelet transforms; Electromyography; Entropy; Feature extraction; Muscles; Amyotrophic lateral sclerosis; discrete wavelets transform; electromyography; myopathy; plexus brachialis;
Conference_Titel :
Image, Signal Processing and Artificial Vision (STSIVA), 2014 XIX Symposium on
Conference_Location :
Armenia
DOI :
10.1109/STSIVA.2014.7010171