Sparse principal component extraction and classification of long-term biomedical signals

This article focuses on finding a solution of sparse representation for signal classification in long-term observational studies. An approach that involves sparse principal component analysis (SPCA) is proposed. This method first uses a non-overlapping moving window for signal segmentation and makes use of SPCA to select a limited number of signal segments for constructing sparse principal components. A set of supervised predictive models based on sparse principal components of training signal segments is then constructed for signal approximation. Within this approach, their model residuals are estimated and used for signal classification. A nearly perfect classification accuracy is obtained for both the synthetic data and EEG signals that we considered. This highly positive result suggests that the proposed method may be useful for automatic event detection in long-term observational signals.