Electrocardiogram signal pattern recognition using PCA and ICA on different databases for improved health management

Abstract

Due to non-invasive and easy to acquire procedure, electrocardiogram (ECG) is broadly adopted for extracting the correct heart health status of the subject (patient). In this paper, a method of analysing ECG signals (based on R-peak detection) recorded during different postures (i.e., sitting, standing and supine) has been presented. The results of this analysis are also compared with standard physioNet database (MIT-BIH arrhythmia database) for validation. Real time ECG signals in all three postures were acquired for 500 subjects, out of which signals of 17 subjects are used for analysis. For filtering these subjects' recordings, existing techniques need a higher order of analogue and digital filters. It increases the complexity of the system, which motivated us to use the combination of principal component analysis (PCA) and independent component analysis (ICA). This combination fulfils the need of higher order filters (HOFs). PCA is used for dimension reduction, whereas ICA is used for noise removal.