Neural Principal Component Analysis for ECG Signal Monitoring
Hanen Chaouch Jebril, Khaled Ouni
Electronic Devices, Vol. 2, No. 2, 2013.
DOI: 
Link: http://www.dline.info/ed/fulltext/v2n2/1.pdf
Abstract:
In this paper, we address the problem of monitoring the cardiovascular system through the integration of automatic tools. This monitoring is to detect heart diseases starting from the electrocardiographic signal (ECG). In particular, we propose a method for detection of defects in the ECG signal analysis exploring the non linear principal components (NLPCA). The data matrix consists of 528 measurements and 9 variables, these variables are determined from waves and segments of the ECG. The proposed approach allows to reduce the size of the data matrix and find the linear and non-linear relationships between variables. Defect detection is established by the statistical SPE (square predictive error) which is based on residues. Defective variables are found by calculating their contributions, its have the highest contributions.