Contactless monitoring of heart and respiratory rate in anesthetized pigs using infrared thermography

Abstract

Pig experiments have played an important role in medical breakthroughs during the last century. In fact, pigs are one of the major animal species used in translational research, surgical models and procedural training due to their anatomical and physiological similarities to humans. To ensure high bioethical standards in animal trials, new directives have been implemented, among others, to refine the procedures and minimize animals' stress and pain. This paper presents a contactless motion-based approach for monitoring cardiorespiratory signals (heart rate and respiratory rate) in anesthetized pigs using infrared thermography. Heart rate monitoring is estimated by measuring the vibrations (precordial motion) of the chest caused by the heartbeat. Respiratory rate, in turn, is computed by measuring the mechanical chest movements that accompany the respiratory cycle. To test the feasibility of this approach, thermal videos of 17 anesthetized pigs were acquired and analyzed. A high agreement between infrared thermography and a gold standard (electrocardiography and capnography-derived respiratory rate) was achieved. The mean absolute error averaged 3.43 ± 3.05 bpm and 0.27 ± 0.48 breaths/min for heart rate and respiratory rate, respectively. In sum, infrared thermography is capable of assessing cardiorespiratory signals in pigs. Future work should be conducted to evaluate infared thermography capability of capturing information for long term monitoring of research animals in a diverse set of facilities.

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.

Fig 3. Correlation plot and Bland-Altman plot comparing HR assessed with infrared thermography (HR_IRT) and HR assessed using ECG (HR_GS).

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).

Fig 4. Correlation plot and Bland-Altman plot comparing RR assessed with infrared thermography (RR_IRT) and RR assessed using the ventilator (RR_GS).

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).