Fusion Classification of Stroke Patients' Biosignals by Weighted Cross-Validation-based Feature Selection (W-CVFS) Method

Abstract A multi-source information fusion-based disease class classification of stroke patients was implemented to address the low classification accuracy of pure input motion and electromyographic signals. sEMG sensor MYO arm ring and wearable wireless motion sensor Shimmer were used as data acquisition devices. The Butterworth high-pass filter filtering and envelope thresholding method detected the activity segment. Detection and FIR filtering using the window function method remove interference from the motion signal. A weighted cross-validation-based feature selection (W-CVFS) method is proposed for feature fusion selection. The top 10 features selected by the W-CVFS method and all 18 features are input to the deep neural network for training and testing, and the feature classification result of the W-CVFS method is 79.17%, which is better than the existing mRMR method (66.67%) and ILFS method (62.50%). The classification accuracy of multi-source information fusion was 95.385%, which was higher than that of a single input motion signal or sEMG. The experiments showed that the proposed method can retain the features that have more influence on the classification results and can improve the classification accuracy of the rehabilitation model for stroke patients.