Knee joint vibroarthrographic (VAG) signals acquired from extensive movements of the knee joints provide insight about the current pathological condition of the knee. VAG signals are non-stationary, aperiodic and non-linear in nature. This investigation has focussed on analyzing VAG signals using Ensemble Empirical Mode Decomposition (EEMD) and modeling a reconstructed signal using Detrended Fluctuation Analysis (DFA). In the proposed methodology, we have used the reconstructed signal and

more »

... ted entropy based measures as features for training semi-supervised learning classifier models. Features such as Tsallis entropy, Permutation entropy and Spectral entropy were extracted as a quantified measure of the complexity of the signals. These features were converted into training vectors for classification using Random Forest. This study has yielded an accuracy of 86.52% while classifying signals. The proposed work can be used in non-invasive pre-screening of knee related issues such as articular damages and chondromalacia patallae as this work could prove to be useful in classification of VAG signals into abnormal and normal sets.