Comparison of AR parametric methods with subspace-based methods for EMG   signal classification using stand-alone and merged neural network models

MEHMET RECEP BOZKURT; ABDÜLHAMİT SUBAŞI; ETEM KÖKLÜKAYA; MUSTAFA YILMAZ

Article Details

Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models

Authors:

MEHMET RECEP BOZKURT | ABDÜLHAMİT SUBAŞI | ETEM KÖKLÜKAYA | MUSTAFA YILMAZ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 3, pp.1547-1559

Document Type:

Article

Publication Date:

5/2016

Document Language:

Turkish

Document ID (Org No.):

2000742122

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

This research introduces an electromyogram (EMG) pattern   classification of individual motor unit action potentials (MUPs) from   intramuscular electromyographic signals. The presented technique   automatically classifies EMG patterns into healthy, myopathic, or neurogenic   categories. To extract a feature vector from the EMG signal, we use   different autoregressive (AR) parametric methods and subspace-based methods.   The proposal was validated using EMG recordings composed of 1200 EMG   patterns obtained from 7 healthy, 7 myopathic, and 13 neurogenic-disordered   people. A feedforward error backpropagation artificial neural network   (FEBANN) and combined neural network (CNN) were used for classification,   where the success rate was slightly higher in CNN. Among the different AR   and subspace methods used in this study, the highest performance was   obtained with the eigenvector method. The following rates were the results   achieved by using the CNN. The correct classification rate for EMG patterns   was 97% for healthy, 93% for myopathic, and 92% for neurogenic   patterns. The obtained accuracy for EMG signal classification is   approximately 94% for CNN. The rates for FEBANN were as follows: 97%   for healthy patterns, 92% for myopathic patterns, and 91% for   neurogenic patterns. The obtained accuracy was 93.3%. By directly using   raw EMG signals, EMG classifications of healthy, myopathic, or neurogenic   classes are automatically addressed.

Article Details

Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models

Authors: MEHMET RECEP BOZKURT | ABDÜLHAMİT SUBAŞI | ETEM KÖKLÜKAYA | MUSTAFA YILMAZ

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 3, pp.1547-1559

Document Type: Article

Publication Date: 5/2016

Document Language: Turkish

Document ID (Org No.): 2000742122

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

MEHMET RECEP BOZKURT | ABDÜLHAMİT SUBAŞI | ETEM KÖKLÜKAYA | MUSTAFA YILMAZ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 3, pp.1547-1559

Document Type:

Article

Publication Date:

5/2016

Document Language:

Turkish

Document ID (Org No.):

2000742122

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632