Entropy-based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise

Non-invasive methods accomplished by a computer aided diagnosis of knee-joint disorders provide an effective tool. The objective of this study is to analyse vibroarthographic (VAG) signals using non-linear signal processing technique. This study includes different entropy-based feature extraction techniques to attain highly distinguishable features. The authors proposed to use a non-linear method known as complete ensemble empirical mode decomposition with adaptive white noise to decompose the VAG signals into intrinsic mode functions (IMFs). Entropy-based features involving approximate entropy, sample entropy, Shannon entropy, Renyi entropy, Tsallis entropy and permutation entropy (PeEn) are computed from dominant IMFs and reconstructed VAG signals. These extracted features are given as input to the least squares support vector machine as a classifier. The results illustrated that PeEn performed better with respect to other entropies. PeEn gives a classification accuracy of 86.61% and Matthews correlation coefficient of 0.7082. The computational complexity of entropies was also analysed. Results inferred that PeEn has a computational complexity of O(N) provided a simple, robust and low computational feature extraction technique. Analysis of VAG signals using non-linear preprocessing and entropy-based features can provide highly distinguishable features for accurate detection of knee-joint disorders.