Detection of neuro mascular disease using EMG signals in wavelet domain

Neuromuscular disorders affects the nerves which impairs the function of muscles. EMG signals are used to diagnose the different neuromuscular diseases. In this proposed method neuromuscular disease is detected by using different features in wavelet domain by using continuous wavelet transform and according to p-value score most discriminatory features were selected. Some features of EMG signals such as maximum amplitude and mean of amplitude, root mean square value are strategically quantified and classified by using support vector machine (SVM) classifier to automate the diagnosis of amyotrophic lateral sclerosis disease. The proposed method tested on EMG database created under EMG Lab, United States and results are encouraging which gives accuracy of 93.75%.

[1]  L. Rowland,et al.  Amyotrophic lateral sclerosis: Theories and therapies , 1994, Annals of neurology.

[2]  Mika P. Tarvainen,et al.  Extraction of typical features from surface EMG signals in Parkinson's disease , 2007 .

[3]  Wei-Ping Zhu,et al.  Identification of motor neuron disease using wavelet domain features extracted from EMG signal , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[4]  Onur Osman,et al.  Feature extraction and classification of neuromuscular diseases using scanning EMG , 2014, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings.

[5]  Paolo Bonato,et al.  Motor unit firing characteristics in patients with amyotrophic lateral sclerosis , 2009, 2009 IEEE 35th Annual Northeast Bioengineering Conference.

[6]  S. A. Fattah,et al.  Neuromuscular disease classification based on mel frequency cepstrum of motor unit action potential , 2014, 2014 International Conference on Electrical Engineering and Information & Communication Technology.

[7]  Umi Kalthum Ngah,et al.  Image classification of brain MRI using support vector machine , 2011, 2011 IEEE International Conference on Imaging Systems and Techniques.

[8]  Anushikha Singh,et al.  Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image , 2016, Comput. Methods Programs Biomed..

[9]  Andrew Eisen,et al.  Amyotrophic lateral sclerosis: A 40-year personal perspective , 2009, Journal of Clinical Neuroscience.

[10]  H. T. Patil,et al.  Neuromuscular disease classification by wavelet decomposition technique , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).