Classification of Cervical Disc Herniation Disease using Muscle Fatigue based surface EMG signals by Artificial Neural Networks
| dc.contributor.author | Ozmen, Guzin | |
| dc.contributor.author | Ekmekcı, Ahmet Hakan | |
| dc.date.accessioned | 2020-03-26T19:32:33Z | |
| dc.date.available | 2020-03-26T19:32:33Z | |
| dc.date.issued | 2017 | |
| dc.department | Selçuk Üniversitesi | en_US |
| dc.description.abstract | This study presents the classification of cervical disc herniationpatientsand healthy persons by using muscle fatigue information. Cervical disc herniationpatients suffer from neck pain and muscle fatigue in the neck increases these aches.Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this studysurface Electromyography (EMG) signals wereused to examine muscle fatigue. EMG signals wereobtained from Trapezius and Sternocleidomastoid(SCM)muscles in the cervical region of 10 control subject and10 cervical disc herniation patients. Surface EMG waspreferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue wasdetermined using Median and Mean frequency values obtained by Fourier Transform and Welch methods.Feature extraction wasthe third step which was performed byShort Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR). Finally, Artificial Neural Network (ANN)was used for classification. Training and test data werecreated by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigatedon patient classification using muscle fatigue information.The best results were obtained by ARmethodwith %99 classification accuracy.Also, the best resultswereobtained by DWT with %100 classification accuracyforSCMmuscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMGsignals by providinghigh accuracyclassification with artificial intelligence methods forcervical disc herniationdisease. Besides, it is shown that muscle fatigue distinguishescervical disc herniationpatientsfrom healthy people | en_US |
| dc.identifier.citation | Ozmen G., Ekmekcı A. H. (2017). Classification of Cervical Disc Herniation Disease using Muscle Fatigue based surface EMG signals by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 256-262. | |
| dc.identifier.endpage | 262 | en_US |
| dc.identifier.issn | 2147-6799 | en_US |
| dc.identifier.issn | 2147-6799 | en_US |
| dc.identifier.issue | 4 | en_US |
| dc.identifier.startpage | 256 | en_US |
| dc.identifier.uri | http://www.trdizin.gov.tr/publication/paper/detail/TWpjeE9EYzNOdz09 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/34490 | |
| dc.identifier.volume | 5 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | International Journal of Intelligent Systems and Applications in Engineering | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | Bilgisayar Bilimleri | en_US |
| dc.subject | Yapay Zeka | en_US |
| dc.title | Classification of Cervical Disc Herniation Disease using Muscle Fatigue based surface EMG signals by Artificial Neural Networks | en_US |
| dc.type | Article | en_US |
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