Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography

Year 2018, Volume: 31 Issue: 1, 100 - 110, 01.03.2018

Rukiye Uzun , Okan Erkaymaz , İrem Senyer Yapici

Abstract

The surface electromyography (sEMG) is useful tool
to diagnose of knee disorder in clinical environments. It assists in designing
the clinical decision support systems based classification. These systems
exhibit complex structure because of sEMG data obtained at different postures
at this study. In this context, we have researched the classification
performance of each posture using artificial neural network (ANN) and logistic
regression (LR) models and have showed that the classification success of the
model used sitting posture data is higher than other postures (gait and
standing). We have promoted this finding by using machine learning and
statistical methods. The results show that the proposed models can classify
with over 95% of success, and also the ANN model has higher performance than
the LR model. Our ANN model outperforms reported studies in literature. The
accuracy results indicate that the models used the only sitting posture data
can exhibit successful classification for the knee disorder. Therefore, the
usage of complex dataset is prevented for diagnosing knee disorder.

Keywords

Artificial neural network, Computer aided diagnosis, Discrete wavelet transform, Surface electromyography

References

• Yalçın İşler, email: [e-mail]
• Umut Orhan, email:[e-mail]
• Matjaz Perc, email:[e-mail]