Title :
Sparse principal component extraction and classification of long-term biomedical signals
Author :
Xie, Shengkun ; Krishnan, Sridhar ; Lawniczak, Anna T.
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
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.
Keywords :
approximation theory; diseases; electroencephalography; learning (artificial intelligence); medical expert systems; medical signal detection; medical signal processing; principal component analysis; signal classification; EEG signals; SPCA; automatic event detection; computer based medical diagnosis; human diseases; long-term biomedical signal classification; nonoverlapping moving window; signal approximation; signal segmentation; sparse principal component analysis; sparse principal component classification; sparse principal component extraction; sparse representation; supervised predictive models; synthetic data; Brain modeling; Feature extraction; Load modeling; Loading; Predictive models; Principal component analysis; Vectors;
Conference_Titel :
Computer-Based Medical Systems (CBMS), 2012 25th International Symposium on
Conference_Location :
Rome
Print_ISBN :
978-1-4673-2049-8
DOI :
10.1109/CBMS.2012.6266371