iPPG Methods Research Articles

Simulating cardiac signals on 3D human models for photoplethysmography development.

Introduction: Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge. Methods: In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos. Results: To compare with different situations in the real world, four common scenarios (stillness, expression/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans. Discussion: This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.

Photoplethysmographic imaging and analysis of pulsatile pressure wave in palmar artery at 10 wavelengths.

As a noncontact method, imaging photoplethysmography (iPPG) may provide a powerful tool to measure pulsatile pressure wave (PPW) in superficial arteries and extract biomarkers for monitoring of artery wall stiffness. We intend to develop a approach for extraction of the very weak cardiac component from iPPG data by identifying locations of strong PPW signals with optimized illumination wavelength and determining pulse wave velocity (PWV). Monochromatic in vivo iPPG datasets have been acquired from left hands to investigate various algorithms for retrieval of PPW signals, distribution maps and waveforms, and their dependence on arterial location and wavelength. A robust algorithm of pixelated independent component analysis (pICA) has been developed and combined with spatiotemporal filtering to retrieve PPW signals. Spatial distributions of PPW signals have been mapped in 10 wavelength bands from 445 to 940nm and waveforms were analyzed at multiple locations near the palmar artery tree. At the wavelength of 850nm selected for timing analysis, we determined PWV values from 12 healthy volunteers in a range of 0.5 to 5.8m/s across the hand region from wrist to midpalm and fingertip. These results demonstrate the potentials of the iPPG method based on pICA algorithm for translation into a monitoring tool to characterize wall stiffness of superficial artery by rapid and noncontact measurement of PWV and other biomarkers within 10s.

Noninvasive Non-Contact SpO2 Monitoring Using an Integrated Polarization-Sensing CMOS Imaging Sensor.

Background:In the diagnosis and primary health care of an individual, estimation of the pulse rate and blood oxygen saturation (SpO) is critical. The pulse rate and SpO are determined by methods including photoplethysmography (iPPG), light spectroscopy, and pulse oximetry. These devices need to be compact, non-contact, and noninvasive for real-time health monitoring. Reflection-based iPPG is becoming popular as it allows non-contact estimation of the heart rate and SpO. Most iPPG methods capture temporal data and form complex computations, and thus real-time measurements and spatial visualization are difficult. Method:In this research work, reflective mode polarized imaging-based iPPG is proposed. For polarization imaging, a custom image sensor with wire grid polarizers on each pixel is designed. Each pixel has a wire grid of varying transmission axes, allowing phase detection of the incoming light. The phase information of the backscattered light from the fingertips of 12 healthy volunteers was recorded in both the resting as well as the excited states. These data were then processed using MATLAB 2021b software. Results: The phase information provides quantitative information on the reflection from the superficial and deep layers of skin. The ratio of deep to superficial layer backscattered phase information is shown to be directly correlated and linearly increasing with an increase in the SpO and heart rate. Conclusions: The phase-based measurements help to monitor the changes in the resting and excited state heart rate and SpO in real time. Furthermore, the use of the ratio of phase information helps to make the measurements independent of the individual skin traits and thus increases the accuracy of the measurements. The proposed iPPG works in ambient light, relaxing the instrumentation requirement and helping the system to be compact and portable.

Simultaneous Estimation of Instantaneous Heart and Respiratory Rates using Image Photoplethysmography on a Single Smartphone.

Heart rate (HR) and respiratory rate (RR) are very important physiological variables useful to evaluate the cardiorespiratory system. At present, there is a great interest by the general population in knowing their health status, quickly and easily. Accordingly, several approaches have been proposed to achieve that goal. In this study, the simultaneous estimation of the instantaneous HR and RR values was achieved by the image photoplethysmography (iPPG) technique, in the contact mode directly implemented in a smartphone. iPPG results were compared with those obtained using specialized biomedical sensors such as the electrocardiogram and the respiratory effort band. Performance evaluation included three different respiratory maneuvers in five healthy volunteers. The absolute mean error for instantaneous HR and RR estimations reached 0.94 ± 0.28 beats per minute and 0.40 ± 0.11 breaths per minute, respectively. The mean correlation index was 0.69 ± 0.14 between the iPPG-derived respiratory signal and the respiratory effort reference signal.Clinical Relevance- These results appear to indicate that the contact iPPG method implemented directly on the smartphone is a good option, accessible to the common population to estimate the instantaneous HR and RR values outside specialized clinical environments, e.g., in the point-of-contact office.

A New Framework for Robust Heart Rate Measurement Based on the Head Motion State Estimation.

It is of great significance in managing human health, preventing and curing diseases such as heart disease to measure and monitor the physiological parameters accurately and robustly. However, imaging photoplethysmography (iPPG) can be easily affected by the ambient illumination variations or the subject's motions. In this paper, therefore, a novel framework of heart rate (HR) measurement robust to both illumination and motion artefacts is proposed, which combines the projection-plane-switching-based iPPG method (2PS) with the singular spectrum analysis (SSA). Based on the estimation of the head motion state, one reasonable projection plane is firstly determined, the temporally normalized red-green-blue signals are projected onto the plane and a pulse signal is obtained by alpha-tuning. After that, singular spectrum analysis (SSA) is applied to the obtained pulse signal and the normalized B-channel signal of the facial region of interest (ROI) to remove the artefacts remained in the pulse signal. For the self-collected database and the public PURE database, Bland-Altman plots show that the proposed 2PS-SSA has better agreement than the five compared methods, where the mean biases are 0.59 beat per minute (bpm) and 0.034 bpm, with 95% limits from -2.59 bpm to 3.78 bpm and from -1.97 bpm to 2.04 bpm, respectively.

Remote Heart Rate Measurement From Near-Infrared Videos Based on Joint Blind Source Separation With Delay-Coordinate Transformation

Noncontact and low-cost heart rate (HR) measurement based on imaging photoplethysmography (iPPG) technology is commonly desired for health care monitoring. However, the usually employed red-green-blue (RGB) cameras are sensitive to illumination variations and cannot work under dark situations. In this study, we propose a novel framework of applying joint blind source separation with delay-coordinate transformation (DCT-JBSS) to evaluate HR from a single-channel near-infrared (NIR) camera in dark situation. First, three facial regions of interest (ROIs) are determined by face detection technique and a single-channel signal is constructed through a frame-by-frame pixel averaging within each ROI. Second, each single-channel signal is transformed into time-delayed multichannel signal through DCT and then treated as a separate ROI signal set. Third, the three ROI signal sets are simultaneously processed by JBSS to derive the underlying shared HR source component vector (SCV), which is usually ordered the first and has the highest correlation across each signal set. Finally, the fast Fourier transform (FFT) is applied to the HR SCV and the corresponding dominant frequency (within the range from 0.7 to 2.5 Hz) with the highest signal-to-noise ratio (SNR) is determined as the target HR frequency. The proposed framework, as well as several other typical iPPG methods, is validated on public DROZY and MR-NIRP databases. The proposed method achieves the best performance, providing a probable way to widen the application of remote and continuous HR measurement during night conditions.

Stress levels estimation from facial video based on non-contact measurement of pulse wave

In this paper, we propose a method to measure multiple stress levels using pulse wave extracted from a facial video. Stress is a crucial factor which triggers mental illnesses. For the prevention, it is necessary to measure and handle with stress. Many researchers have attempted to measure stress using with a contact sensor. However, contact sensors can be uncomfortable or unsafe depending on the person. Recently, imaging photoplethysmography (iPPG), which measures pulse wave without contact sensors has been gathering interest. However, the estimatability of pulse rate variability (PRV) on stress condition, which include information of stress has not been investigated in most papers. In addition, the stress measurement based on PRV only detect relaxation and stress. Therefore, in this study, the following contributions is made. First, we investigate the effectiveness of the PRV of the several iPPG methods on stress condition. It is noted that comparing several iPPG methods on the stress condition is not achieved in the previous works. Second, we propose a method to estimate 4 stress levels based on PRV features based on a facial video with the iPPG methods. It is noted that “multiple” stress levels estimation based on ‘a facial video’ has not been accomplished in the previous works in the iPPG method.

Novel instrumental markers of proximal scleroderma provided by imaging photoplethysmography

Objective: Instrumental identification of proximal scleroderma, which is necessary for the early diagnosis of systemic sclerosis (SSD), has not yet been developed. The aim of this study was to assess the potential diagnostic value of the imaging photoplethysmography (IPPG) method in patients with SSD. Approach: The study enrolled 19 patients with SSD and 21 healthy subjects matched by age and sex with the patients. Spatial distribution of capillary-blood-flow parameters and their dynamics was estimated in the facial area of patients and subjects. In the IPPG system, a 40 s video of the subject’s face illuminated by green polarized light was recorded with a monochrome digital camera in synchronization with the electrocardiogram. Experimental data were processed using custom software allowing assessment of an arrival time of the blood pressure wave (PAT), an amplitude of pulsatile component (APC) of the photoplethysmographic (PPG) waveform, and their variability. Main results: Our study has revealed a significant increase in PAT variability in patients with SSD compared to the control group: 52 ± 47 ms vs 24 ± 13 ms (P =0.01). Similarly, the variability of the PPG-pulse shape was larger in patients with SSD: 0.13 ± 0.07% vs 0.09 ± 0.02% (P < 0.001). In addition, patients with scleroderma showed a significantly greater degree of asymmetry of the APC parameter than the control group: 17.7 ± 9.7 vs 7.9 ± 5.0 (P < 0.001). At the same time, no correlation was found between the PPG waveform parameters and either the form or duration of the disease. Also, no relationship between the characteristics of the PPG waveform and the modified Rodnan skin score was found. Significance: Novel instrumental markers found in our pilot study showed that the IPPG method can be used for diagnosing SSD in the early stages of the disease.

Contactless Monitoring of Microcirculation Reaction on Local Temperature Changes

Assessment of skin blood flow is an important clinical task which is required to study mechanisms of microcirculation regulation including thermoregulation. Contactless assessment of vasomotor reactivity in response to thermal exposure is currently not available. The aim of this study is to show the applicability of the imaging photoplethysmography (IPPG) method to measure quantitatively the vasomotor response to local thermal exposure. Seventeen healthy subjects aged 23 ± 7 years participated in the study. A warm transparent compress applied to subject’s forehead served as a thermal impact. A custom-made IPPG system operating at green polarized light was used to monitor the subject’s face continuously and simultaneously with skin temperature and electrocardiogram (ECG) recordings. We found that the thermal impact leads to an increase in the amplitude of blood pulsations (BPA) simultaneously with the skin temperature increase. However, a multiple increase in BPA remained after the compress was removed, whereas the skin temperature returned to the baseline. Moreover, the BPA increase and duration of the vasomotor response was associated with the degree of external heating. Therefore, the IPPG method allows us to quantify the parameters of capillary blood flow during local thermal exposure to the skin. This proposed technique of assessing the thermal reactivity of microcirculation can be applied for both clinical use and for biomedical research.

Motion-tolerant heart rate estimation from face videos using derivative filter

Imaging photoplethysmography (IPPG) technique allows us to extract blood volume pulse (BVP) signals from face videos for measuring heart rate (HR), which is useful in applications such as neonatal monitoring, telemedicine and affective computing. Because the BVP signal is small, the HR estimation results are sensitive to face motion disturbance caused by spontaneous head movements and facial expressions of subjects. In this paper, we design a novel filtering method for refining the RGB signals with motion artifacts. Based on the observation that subtle color changes of face skin are smoother than large face motions at temporal scale, we use the three-order derivative of Gaussian filter to select subtle color changes under large motions. Our method is validated on both our self-collected dataset and public dataset MAHNOB-HCI containing face videos with head movements and facial expressions. By employing the proposed filtering method to pre-process the RGB signals before BVP signal extraction, a range of IPPG methods are improved to generate robust HR estimation results under realistic situations.

Algorithms for Monitoring Heart Rate and Respiratory Rate From the Video of a User's Face.

Smartphone cameras can measure heart rate (HR) by detecting pulsatile photoplethysmographic (iPPG) signals from post-processing the video of a subject’s face. The iPPG signal is often derived from variations in the intensity of the green channel as shown by Poh et. al. and Verkruysse et. al.. In this pilot study, we have introduced a novel iPPG method where by measuring variations in color of reflected light, i.e., Hue, and can therefore measure both HR and respiratory rate (RR) from the video of a subject’s face. This paper was performed on 25 healthy individuals (Ages 20–30, 15 males and 10 females, and skin color was Fitzpatrick scale 1–6). For each subject we took two 20 second video of the subject’s face with minimal movement, one with flash ON and one with flash OFF. While recording the videos we simultaneously measuring HR using a Biosync B-50DL Finger Heart Rate Monitor, and RR using self-reporting. This paper shows that our proposed approach of measuring iPPG using Hue (range 0–0.1) gives more accurate readings than the Green channel. HR/Hue (range 0–0.1) (n}{}r=0.9201n, n}{}pn-value = 4.1617, and RMSE = 0.8887) is more accurate compared with HR/Green (n}{}r=0.4916n, n}{}pn-value = 11.60172, and RMSE = 0.9068). RR/Hue (range 0–0.1) (n}{}r=0.6575n, n}{}pn-value = 0.2885, and RMSE = 3.8884) is more accurate compared with RR/Green (n}{}r=0.3352n, n}{}pn-value = 0.5608, and RMSE = 5.6885). We hope that this hardware agnostic approach for detection of vital signals will have a huge potential impact in telemedicine, and can be used to tackle challenges, such as continuous non-contact monitoring of neo-natal and elderly patients. An implementation of the algorithm can be found at https://pulser.thinkbiosolution.com