The PhysioCam: A Novel Non-Contact Sensor to Measure Heart Rate Variability in Clinical and Field Applications

Maria I Davila, Gregory F Lewis, Stephen W Porges, Maria I Davila, Gregory F Lewis, Stephen W Porges

Abstract

Heart rate variability (HRV) is a reliable indicator of health status and a sensitive index of autonomic stress reactivity. Stress negatively affects physical and psychological wellness by decreasing cardiovascular health and reducing quality of life. Wearable sensors have made it possible to track HRV during daily activity, and recent advances in mobile technology have reduced the cost and difficulty of applying this powerful technique. Although advances have made sensors smaller and lighter, some burden on the subject remains. Chest-worn electrocardiogram (ECG) sensors provide the optimal source signal for HRV analysis, but they require obtrusive electrode or conductive material adherence. A less invasive surrogate of HRV can be derived from the arterial pulse obtained using the photoplethysmogram (PPG), but sensor placement requirements limit the application of PPG in field research. Factors including gender, age, height, and weight also affect PPG-HRV level, but PPG-HRV is sufficient to track individual HRV reactions to physical and mental challenges. To overcome the limitations of contact sensors, we developed the PhysioCam (PhyC), a non-contact system capable of measuring arterial pulse with sufficient precision to derive HRV during different challenges. This passive sensor uses an off the shelf digital color video camera to extract arterial pulse from the light reflected from an individual's face. In this article, we validate this novel non-contact measure against criterion signals (ECG and PPG) in a controlled laboratory setting. Data from 12 subjects are presented under the following physiological conditions: rest, single deep breath and hold, and rapid breathing. The following HRV parameters were validated: interbeat interval (IBI), respiratory sinus arrhythmia (RSA), and low frequency HRV (LF). When testing the PhyC against ECG or PPG: the Bland-Altman plots for the IBIs show no systematic bias; correlation coefficients (all p values < 0.05) comparing ECG to PhyC for IBI and LF approach 1, while RSA correlations average 0.82 across conditions. We discuss future refinements of the HRV metrics derived from the PhyC that will enable this technology to unobtrusively track indicators of health and wellness.

Keywords: arterial pulse; heart rate variability; non-contact monitoring; optics and physiology; sensors agreement.

Figures

Figure 1
Figure 1
Schematic representation of the relative light absorption of the main chromophores of the human skin: Hb (deoxygenated hemoglobin), HbO2 (oxygenated hemoglobin), and melanin. Bottom color bar: spectral response range for digital video cameras [adapted from Prahl (23) and Huang et al. (24)].
Figure 2
Figure 2
Physiological signals. Plot of 15 s of data for participant #01 during 1BSL, the green line represents the elPPG, the gray line is the PhysioCam (PhyC), and the blue line is the electrocardiogram (ECG).
Figure 3
Figure 3
Bland–Altman and scatter plot for interbeat interval (IBI) from the electrocardiogram (ECG) and PhysioCam (PhyC), color coded by participant. (A) Plot of the IBI differences vs the means for the ECG and PhyC. Red lines indicate the 95% confidence interval. (B) Scatter plot of the PhyC vs ECG IBIs.
Figure 4
Figure 4
Bland–Altman and scatter plot for interbeat interval (IBI) from the elPPG and PhysioCam (PhyC), color coded by participant. (A) Plot of the IBI differences vs the means for the elPPG and PhyC. Red lines indicate the 95% confidence interval. (B) Scatter plot of the PhyC vs elPPG IBIs.
Figure 5
Figure 5
Bland–Altman and scatter plot for interbeat interval (IBI) from the electrocardiogram (ECG) and elPPG, color coded by participant. (A) Plot of the IBI differences vs the means for the ECG and elPPG. Red lines indicate the 95% confidence interval. (B) Scatter plot of the elPPG vs ECG IBIs.
Figure 6
Figure 6
Scatter plot for average window interbeat interval (IBI) from the electrocardiogram (ECG) and PhysioCam (PhyC) color marked by participant. (A) Scatter plot of the PhyC vs ECG for 2 s average windows IBIs. (B) Scatter plot of the PhyC vs ECG for 5 s average windows.
Figure 7
Figure 7
Scatter plots between sensors for heart period (HP), color coded by participant. (A) PhysioCam (PhyC) vs electrocardiogram (ECG). (B) PhyC vs elPPG. (C) elPPG vs ECG.
Figure 8
Figure 8
Scatter plots between sensors for respiratory sinus arrhythmia (RSA), color coded by participant. (A) PhysioCam (PhyC) vs electrocardiogram (ECG). (B) PhyC vs elPPG. (C) elPPG vs ECG.
Figure 9
Figure 9
Scatter plots between sensors for LF, color coded by participant. (A) PhysioCam (PhyC) vs electrocardiogram (ECG). (B) PhyC vs elPPG. (C) elPPG vs ECG.
Figure 10
Figure 10
Mean of changes from the 1BSL (error bars: ±2 SE) for the three sensors electrocardiogram (ECG), elPPG, and PhysioCam (PhyC). (A) Heart period (HP). (B) Respiratory sinus arrhythmia (RSA). (C) LF (error bars: ±2 SE).

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