konjes Konya Journal of Engineering Sciences 2667-8055 Konya Technical University 10.36306/konjes.1185629 Engineering Mühendislik PREDICTION OF CERVICAL DISC HERNIATION DISEASE UTILIZING TRAPEZIUS sEMG SIGNALS WITH MACHINE LEARNING TECHNIQUES BASED ON FREQUENCY DOMAIN FEATURE EXTRACTION Frekans Alanı Öznitelik Çıkarımına Dayalı Makine Öğrenme Teknikleri ile Trapezius Yüzey EMG Sinyallerini Kullanarak Servikal Disk Fıtığı Hastalığının Tahmini https://orcid.org/0000-0001-5549-8385 Yılmaz Burak KTO KARATAY UNIVERSITY, FACULTY OF ENGINEERING https://orcid.org/0000-0003-3007-5807 Özmen Güzin SELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ https://orcid.org/0000-0002-5595-7251 Ekmekci Hakan SELÇUK ÜNİVERSİTESİ, TIP FAKÜLTESİ 03 01 2023 11 1 205 219 10 07 2022 12 19 2022 Copyright © 2004, Konya Journal of Engineering Sciences 2004 Konya Journal of Engineering Sciences

Cervical disk herniation (CDH) is a disease that affects the quality of life of many people due to the neck pain it causes. The aim of this study was to develop an automatic prediction system to aid in diagnosis by evaluating the change in the surface electrical activity of the trapezius muscle in SDH disease in order to find an answer to the question: 'Can the surface electromyogram (sEMG) recorded from the trapezius muscle be an effective indicator for the diagnosis of SDH disease?'. To this end, a dataset will be created using preprocessing and feature extraction methods from sEMG signals from CDH patients and healthy individuals. In the first step, the Savitsky-Golay filter is used to denoise the sEMG signals and the dominant frequency signals between 20 and 150 Hz are included in the study using the Butterworth filter design. Twenty PSD-based features in the frequency domain were then obtained from the signals to which we applied the Burg method. Eleven of the most significant features based on the information gain, gain ratio, and Gini values are selected to be submitted to the classifiers. 80% of all new feature areas are used for classification and the rest for prediction. The best classification accuracy of 91.6% was obtained with the Tree classifier using 10-fold cross-validation for classification. In addition, neural networks and CN2 rule inducer provided 87.5% classification accuracy for prediction using 20% of the remaining data that the classifiers had not seen before. The experimental results demonstrate that the trapezius muscle has different surface electrical activity in CDH patients and healthy subjects and that the frequency domain characteristics of this activity are important for disease prediction.

Servikal Disk Hernisi (SDH), neden olduğu boyun ağrısı ile birçok kişinin günlük yaşam kalitesini düşüren bir hastalıktır. Asıl sorumuz şudur: ‘Trapezius kasından alınan yüzey Elektromiyogram (yEMG) sinyali SDH hastalığının tanısında etkili bir gösterge olabilir mi?’. Bu çalışma, SDH hastalığının Trapezius kasındaki yüzey elektriksel aktivite değişimini değerlendirerek tanıya yardımcı olan otomatik bir tahmin sistemi tasarlamayı amaçlamaktadır. Bu amaçla, SDH hastalarından ve sağlıklı deneklerden toplanan yEMG sinyallerinden ön işleme ve özellik çıkarma yöntemleri kullanılarak bir veri seti hazırlanmıştır. İlk aşamada, yEMG sinyallerini gürültüden arındırmak için Savitsky-Golay filtresi kullanılmış ve Butterworth filtre tasarımı ile 20-150 Hz aralığındaki baskın frekans sinyalleri çalışmaya dahil edilmiştir. Daha sonra Burg yöntemi uygulanan sinyallerden frekans alanında yirmi PSD tabanlı öznitelik elde edilmiştir. Bilgi Kazancı, Kazanç Oranı ve Gini değerlerine dayalı en önemli on bir özellik, sınıflandırıcılara sunulmak üzere seçilmiştir. Tüm yeni özellik uzaylarının %80' i sınıflandırma için, geri kalanı ise tahmin için kullanılmıştır. Sınıflandırma için 10 kat çapraz doğrulama uygulanarak Ağaç sınıflandırıcı ile %91.6' lık en iyi sınıflandırma doğruluğu elde edilmiştir. Ayrıca, Sinir ağları ve CN2 kuralı başlatıcısı, sınıflandırıcıların daha önce görmediği kalan verilerin %20' sini kullanarak tahmin için %87.5 sınıflandırma doğruluğu sağlamıştır. Deneysel sonuçlar, trapezius kasının SDH hastalarında ve sağlıklı kişilerde farklı yüzey elektriksel aktivitesine sahip olduğunu ve bu aktivitenin frekans alanı özelliklerinin hastalık tahmininde ayırt edici olduğunu ortaya koymaktadır.

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