Analysis of Knee Joint Vibration Signals Using Ensemble Empirical Mode Decomposition

Abstract 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 extracted 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.