Abstract: Temporomandibular joint Disorder (TMD) is with multifaceted and complex signs and symptoms which makes day to day activities of an individual uneasy. Electromyographic (EMG) processing of related muscles recordings could provide an early and immediate detection of TMD. To detect the TMD using surface electromyography (SEMG) of Masseter and Temporalis muscle with discrete wavelet transform (DWT) using spectral coding. To analyze the data, a new feature selection approach in the spectral domain is proposed. For statistical analyses, SPSS version 24 is employed. The results of the study revealed that the proposed approach was able to improve the accuracy of the classification by implementing a combination of DWT and the Support Vector Machine (SVM). The proposed method also exhibited a significant improvement in its performance in terms of its accuracy with 93%. In addition, the statistical analysis revealed that the model was able to improve the mean rank of the experimental and control group.

Bormane D. S, Roopa B. Kakkeri and R. B. Kakkeri, “Surface Electromyography Signal Classification for the Detection of Temporomandibular Joint Disorder using Spectral Mapping Method” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130860

@article{S2022,
title = {Surface Electromyography Signal Classification for the Detection of Temporomandibular Joint Disorder using Spectral Mapping Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130860},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130860},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Bormane D. S and Roopa B. Kakkeri and R. B. Kakkeri}
}