Non-Contact Remote Measurement of Heart Rate Variability using Near-Infrared Photoplethysmography Imaging

The Novel Estimation Algorithm of Heart Rate Variability and Stress Using Facial Video Analysis

Heart Rate Variability (HRV) can provide extensive information about human health. Calculating HRV requires careful measurement (in milliseconds) of the time interval between each heartbeat. HRV differs among individuals due to varying physiological conditions. We can identify certain pathologies and health issues by accurately detecting changes in these time intervals. HRV is typically detected through ECG signals, however, using video-based Remote Photoplethysmography (rPPG) offers a more convenient and contactless method for calculating HRV. In this study, we propose a system for monitoring HRV using a low-cost camera. We use the Eulerian Video Magnification (EVM) technique. It can magnify subtle changes in the face that cannot be seen by the naked human eye. To improve the accuracy of EVM, a series of signal-processing steps were implemented to enhance the precision of HRV estimations. We evaluate our method on a self-recorded custom dataset and Pulse Rate Detection (PURE) datasets. Compared with other methods, the results show that the proposed method has better performance in HRV analysis.

Noncontact Monitoring of Heart Rate and Heart Rate Variability in Geriatric Patients Using Photoplethysmography Imaging

OBJECTIVE

Geriatric patients, especially those with dementia or in a delirious state, do not accept conventional contact-based monitoring. Therefore, we propose to measure heart rate (HR) and heart rate variability (HRV) of geriatric patients in a noncontact and unobtrusive way using photoplethysmography imaging (PPGI).

METHODS

PPGI video sequences were recorded from 10 geriatric patients and 10 healthy elderly people using a monochrome camera operating in the near-infrared spectrum and a colour camera operating in the visible spectrum. PPGI waveforms were extracted from both cameras using superpixel-based regions of interests (ROI). A classifier based on bagged trees was trained to automatically select artefact-free ROIs for HR estimation. HRV was calculated in the time-domain and frequency-domain.

RESULTS

an RMSE of 1.03 bpm and a correlation of 0.8 with the reference was achieved using the NIR camera for HR estimation. Using the RGB camera, RMSE and correlation improved to 0.48 bpm and 0.95, respectively. Correlation for HRV in the frequency-domain (LF/HF-ratio) was 0.50 using the NIR camera and 0.70 using the RGB camera.

CONCLUSION

We were able to demonstrate that PPGI is very suitable to measure HR and HRV in geriatric patients. We strongly believe that PPGI will become clinically relevant in monitoring of geriatric patients.

SIGNIFICANCE

we are the first group to measure both HR and HRV in awake geriatric patients using PPGI. Moreover, we systematically evaluate the effects of the spectrum (near-infrared vs. visible), ROI, and additional motion artefact reduction algorithms on the accuracy of estimated HR and HRV.

The Role of Heart Rate Variability in the Future of Remote Digital Biomarkers

Heart rate variability (HRV) offers insights into humoral, neural and neurovisceral processes in health and disorders of brain, body and behavior but has yet to be fully potentiated in the digital age. Remote measurement technologies (RMTs), such as, smartphones, wearable sensors or home-based devices, can passively capture HRV as a nested parameter of neurovisceral integration and health during everyday life, providing insights across different contexts, such as activities of daily living, therapeutic interventions and behavioral tasks, to compliment ongoing clinical care. Many RMTs measure HRV, even consumer wearables and smartphones, which can be deployed as wearable sensors or digital cameras using photoplethysmography. RMTs that measure HRV provide the opportunity to identify digital biomarkers indicative of changes in health or disease status in disorders where neurovisceral processes are compromised. RMT-based HRV therefore has potential as an adjunct digital biomarker in neurovisceral digital phenotyping that can add continuously updated, objective and relevant data to existing clinical methodologies, aiding the evolution of current "diagnose and treat" care models to a more proactive and holistic approach that pairs established markers with advances in remote digital technology.

Feasibility of assessing ultra-short-term pulse rate variability from video recordings

Objectives

Remote photoplethysmography (rPPG) is a promising non-contact measurement technique for assessing numerous physiological parameters: pulse rate, pulse rate variability (PRV), respiratory rate, pulse wave velocity, blood saturation, blood pressure, etc. To justify its use in ultra-short-term (UST) PRV analysis, which is of great benefit for several healthcare applications, the agreement between rPPG- and PPG-derived UST-PRV metrics was studied.

Approach

Three time-domain metrics—standard deviation of normal-to-normal (NN) intervals (SDNN), root mean square of successive NN interval differences (RMSSD), and the percentage of adjacent NN intervals that differ from each other by more than 50 ms (pNN50)—were extracted from 56 video recordings in a publicly available data set. The selected metrics were calculated on the basis of three groups of 10 s recordings and their average, two groups of 30 s recordings and their average, and a group of 60 s recordings taken from the full-length recordings and then compared with metrics derived from the corresponding reference (PPG) pulse waveform signals by using correlation and effect size parameters, and Bland–Altman plots.

Main results

The results show there is stronger agreement as the recording length increases for SDNN and RMSSD, yet there is no significant change for pNN50. The agreement parameters reach r = 0.841 (p < 0.001), r = 0.529 (p < 0.001), and r = 0.657 (p < 0.001), estimated median bias −1.52, −2.28 ms and −1.95% and a small effect size for SDNN, RMSSD, and pNN50 derived from the 60 s recordings, respectively.

Significance

Remote photoplethysmography-derived UST-PRV metrics manage to capture UST-PRV metrics derived from reference (PPG) recordings well. This feature is highly desirable in numerous applications for the assessment of one’s health and well-being. In future research, the validity of rPPG-derived UST-PRV metrics compared to the gold standard electrocardiography recordings is to be assessed.