Fig 1.
Schematic overview of the HR algorithm.
A: Selection of regions of interest (ROI). B: Detection and tracking of feature points. C: Extraction of feature points’ trajectories. D: Temporal filtering. E: Blind source separation via principal component analysis (PCA). F: Rank principal components (PCs) based on their variance. G: Computation of frequency spectra and estimation of HR.
Fig 2.
Schematic overview of the RR algorithm.
A: Selection of ROI. B: Detection and tracking of feature points. C: Extraction of feature points’ trajectories. D: Temporal filtering. E: Blind source separation via PCA. F: Rank PCs based on their variance. G: Computation of frequency spectra and estimation of RR.
Table 1.
Results for HR estimation in infrared thermograms from pigs.
Fig 3.
Correlation plot and Bland-Altman plot comparing HR assessed with infrared thermography (HRIRT) and HR assessed using ECG (HRGS).
The plots include the data from all the 17 animals. A: The plot on the left shows an R-squared of 0.9598 and a sum of squared errors of 4.8 bpm. B: The graph on the right presents a bias of -0.14 bpm (solid line), and the 95% limits of agreement vary between -9.3 and 9.0 bpm (dashed lines).
Table 2.
Results for RR estimation in infrared thermograms from pigs.
Fig 4.
Correlation plot and Bland-Altman plot comparing RR assessed with infrared thermography (RRIRT) and RR assessed using the ventilator (RRGS).
The plots include the data from all the 17 animals. A: The plot on the left shows an R-squared of 0.97 and a sum of squared errors of 0.56 breaths/min. B: The graph on the right presents a bias of -0.14 breaths/min (solid line), and the 95% limits of agreement vary between -0.9 and 1.2 breaths/min (dashed lines).