A Canonical Correlation Approach to Heart Beat Detection in Textile ECG Measurements

Research in textile sensors has lead to new ways to measure electrocardiograms (ECG). However, additional disturbances from e.g. muscular noise and high skin-electrode impedances often result in poor signal quality. The paper contains a simple application of canonical correlation analysis (CCA) on multi channel ECG signals recorded with textile electrodes. Using CCA to solve the blind source separation (BSS) problem, we intend to separate the ECG signal from the various noise sources. The method (CCABSS) was compared to simple averaging of the ECG channels and to the independent component analysis method (ICA). A heart beat detector was used to evaluate the signal quality. Results show that the signal was completely lost while simulating various noise in 33%, 17% and 7% of the cases for averaging, ICA and CCA, respectively.