Two-fold feature extraction technique for biomedical signals classification

This presents a two-fold feature extraction technique for biomedical signals classification. In this work, signals are uniformly decomposed to form a set of uniform matrices. Then using Canonical Correlation Analysis (CCA) each pair matrices are mapped to orthogonal space. Next, Wavelet transformation is performed on original feature matrices and then mapped to orthogonal space. Both domains are statistically independent. From each domain, same dimensional feature vectors are extracted and concatenated them to form single embedding vectors. The embedding vectors are fed to classifier to recognize the healthy control and pathological signal patterns. For demonstration, we consider three groups of EMG signal vis Amyotrophic lateral sclerosis (ALS), Myopathy (Myo) and healthy control (Nor). Results indicate that adopted feature extraction technique and synchronization of features strongly enhances the quality of feature pattern. The optimum recognition rate under adopted feature technique are obtained 95.91%±3.6% and 95.58%±1.5 % in Myo-Nor and ALS-Nor respectively. The proposed feature extraction scheme is consistent not only in accuracies but also other quality assessment parameters. Hence it promises to provide a better strategic tool for signal classification.