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DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications

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Abstract

Automated diagnosis of neuromuscular disorders such as myopathy and neuropathy can be done by measuring and analyzing the nonlinear and non-stationary trends in electromyogram (EMG) signals. This paper introduces a new automated diagnostic approach with the combination of discrete wavelet transform (DWT) and maximum likelihood estimation (ML-estimation) for analysis and classification of EMG signals into healthy, myopathy, or neuropathy classes. DWT decomposes the considered EMG signals into frequency sub-bands, and ML-estimation is used for extracting the features from the discrete wavelet sub-bands. Subsequently, obtained feature vectors are used for classifying the EMG signals using multilayer perceptron neural network (MLPNN). Comparative analysis of various performance measures has been performed with all the 71 wavelets of DWT and 13 training algorithms of MLPNN. Results are promising with specificity of 83.33%, sensitivity (myopathy) of 91.66%, sensitivity (neuropathy) of 83.33%, and total classification accuracy of 86.5%, which are obtained using the combination of Daubechies-11 wavelet and Fletcher–Reeves conjugate gradient training algorithm.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

    Shobha Jose

  2. Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

    S. Thomas George

  3. Department of Neurology, St. Thomas Hospital, Changanassery, Kottayam, India

    P. S. Roopchand

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  1. Shobha Jose
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  2. S. Thomas George
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  3. P. S. Roopchand
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Correspondence to S. Thomas George.

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Jose, S., Thomas George, S. & Roopchand, P.S. DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications. SIViP 14, 601–608 (2020). https://doi.org/10.1007/s11760-019-01590-6

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