| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 13
| Issue : 1 | Page : 29-39 |
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Prediction of biceps muscle electromyogram signal using a NARX neural network
Vahid Khodadadi1, Fereidoun Nowshiravan Rahatabad1, Ali Sheikhani1, Nader Jafarnia Dabanloo2
1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
Correspondence Address:
Fereidoun Nowshiravan Rahatabad
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran
Iran
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jmss.jmss_3_22
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Background: This study was conducted to compare the response between the results of experimental data and the results achieved by the NARX neural network model to predict the electromyogram (EMG) signal on the biceps muscle in nonlinear stimulation conditions as a new stimulation model. Methods: This model is applied to design the controllers based on functional electrical stimulation (FES). To this end, the study was conducted in five stages, including skin preparation, placement of recording and stimulation electrodes, along with the position of the person to apply the stimulation signal and recording EMG, stimulation and recording of single-channel EMG signal, signal preprocessing, and training and validation of the NARX neural network. The electrical stimulation applied in this study is based on a chaotic equation derived from the Rossler equation and on the musculocutaneous nerve, and the response to this stimulation, i.e., the EMG signal, is from the biceps muscle as a single channel. The NARX neural network was trained, along with the stimulation signal and the response of each stimulation for 100 recorded signals from 10 individuals, and then validated and retested for trained data and new data after processing and synchronizing both signals. Results: The results indicate that the Rossler equation can create nonlinear and unpredictable conditions for the muscle, and we also can predict the EMG signal with the NARX neural network as a predictive model. Conclusion: The proposed model appears to be a good method to predict control models based on FES and to diagnose some diseases.
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