Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

Abstract

They distinguish information linked with knee-joints’ articular cartilage surfaces feasibly extracted from vibroarthographic (VAG) signals. Even though diverse researches have presented numerous types of specifications for the investigation and classification of VAG signals, minimal methods have been examined to constitute the differentiations between different knee joint disorder levels. In this paper, a multi-class classification method called Intrinsic Three Band Curvilinear Principal Component and Divergence Forest (ITBCPC-DF) is proposed to precisely differentiate between different levels of disorder. First, an Intrinsic Three Band Pre-processing is applied to the input VAG signal images to prevent aliasing and eliminate muscle contraction interference, therefore contributing to de-noised signal images. Next, Principal Time Domain Curvilinear Energy is applied to the de-noised signal images that extract the requisite patterns in the VAG signals associated with the knee-joint position. Finally, Probable Divergent Random Forest is applied for multi-class classification. Compared with other VAG signal analysis methods, the experimental results show that the proposed ITBCPC-DF method shows comparatively better accuracy, sensitivity, specificity, and AUC curve.