An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification

Abstract

In this paper, we present a real-time feature extraction and fusion model for an automated staging of electromyogram signals-generalizing canonical correlation analysis (CCA). The proposed method is capable of capturing multiple view information (i.e., feature matrices) generated from signals. Our algorithm employs an optimization technique to derive sets of statistical features among the paired views based on which possible variations of signals have been demonstrated. Next, discrete wavelet transformation is performed on multiple views to create domain independent views which are then subjected to CCA optimization. The estimated two sets of statistically independent features from two independent analysis are concentrated through two recently proposed fusion models, and then, we evaluate global feature matrices. Further it is validated statistically for $$p<0.05$$ . The proposed algorithm is then analyzed and compared with state-of-the-art methods. Results indicate that the proposed approach outperforms many other methods in terms of accuracy, specificity and sensitivity, which are 98.80, 99.0 and 98.0%, respectively. Thus, the proposed algorithm is suitable for large-scale applications and expedite diagnosis research.