Key result
Principal component analysis (PCA) outperformed independent component analysis (ICA) for extracting cardiac pulse from camera-based photoplethysmograms when using more homogeneous input signals.
For camera-based photoplethysmography, PCA outperforms ICA for homogeneous input signals, and monochrome green inputs yield better signal-to-noise ratios than multi-wavelength inputs.
Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-to-noise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.
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Wedekind et al. (2017) studied postoperative cardiovascular patients (n=18). Blind source separation techniques (PCA and ICA) vs. standard cbPPGs was evaluated on spectral signal-to-noise ratio (SNR) of the cardiac pulse. Principal component analysis (PCA) outperformed independent component analysis (ICA) for extracting cardiac pulse from camera-based photoplethysmograms when using more homogeneous input signals.
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