Detail publikace

Detection of neuro mascular disease using EMG signals in wavelet domain

Originální název

Detection of neuro mascular disease using EMG signals in wavelet domain

Anglický název

Detection of neuro mascular disease using EMG signals in wavelet domain

Jazyk

en

Originální abstrakt

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%.

Anglický abstrakt

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%.

BibTex


@inproceedings{BUT144120,
  author="Radim {Burget}",
  title="Detection of neuro mascular disease using EMG signals in wavelet domain",
  annote="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%.",
  address="IEEE",
  booktitle="2017 IEEE Uttar Pradesh Section International Conference on Electrical",
  chapter="144120",
  doi="10.1109/UPCON.2017.8251121",
  howpublished="online",
  institution="IEEE",
  year="2017",
  month="october",
  pages="624--627",
  publisher="IEEE",
  type="conference paper"
}