Finger Photoplethysmogram Signal Enhancement: Comparing Performance between PCA and ICA Methods

摘要

Pulse signal is prone to corruption with motion artifacts (MA) due to attachment of the sensor to extreme body parts like finger, toes and forehead. This paper compares the performance between two popular statistical signal processing tools, viz., principal component analysis (PCA) with fast independent component analysis (fICA) in reduction of MA from finger pulse signal collected from 30 human volunteers. A multivariate dataset was generated with systolic peak-aligned Photoplethysmogram (PPG) beats extracted from time series data. After eigenvalues decomposition of the covariance matrix, the original data was reconstructed using the first principal component. The mean correlation coefficient of average beat template of ICA preprocessed data and clean data, averaged over 30 volunteers is 0.9876 while that of PCA preprocessed data with clean data is 0.9778. With white Gaussian noise of known SNR, maximum absolute error for PCA preprocessed data is very small, 3.14% from SNR 25dB onwards. It was also found that beat to beat correlation is higher in the PCA preprocessed data.