Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals

Diffuse transmittance spectroscopy for ultra short-term measurement of pulse rate variability in healthy subjects

Multi-wavelength plethysmography (MWPPG) is a growing technique for noninvasive hemodynamic measurements, particularly spectral based methods. Different wavelengths have been investigated for both transmissive and reflectance PPG configurations. The objective of this work is to investigate the feasibility of Diffuse Transmittance Spectroscopy (DTS) for Pulse Rate Variability (PRV) measurements and to evaluate the performance of diffuse transmittance spectroscopy in quantifying ultrashort pulse rate variability. DTS was used for reconstructing PPG signal followed by PRV analysis. DTS and reference PPG recordings were acquired from 18 healthy subjects in total. PRV features include time-domain, and frequency-domain features are extracted from 50 s duration. The extracted PRV parameters were compared to PRV parameters derived from conventional pulse oximetry-based PPG. Pulse rate variability analysis was applied on DTS and reference PPG tracings. The comparison demonstrated a strong correlation between the diffuse transmittance spectral method and the gold standard PPG sensor. Significant correlation (r > 0.90, p < 0.05) was found between PRV from DTS and reference PRV for mean intervals, standard deviation of intervals (SDNN) and the root-mean square of the difference of successive intervals (RMSSD). A good agreement was found between PRV Parameters in time domain of PPG analysis using Bland-Altman plots with 95 % limits of agreements. However, Bland-Altman analysis showed a considerable divergence in frequency parameters. The study also revealed that PPG based diffuse transmittance measurements were insensitive to ambient noise in comparison with conventional pulse oximeter. The results suggest a potential application of the diffuse transmittance spectroscopy for pulse rate variability analysis.

Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate

Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30 s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R2 scores of 0.6699 for 13 NNIs, 0.8751 for 26 NNIs, and 0.9358 for 39 NNIs, surpassing the time-segmented approach, which yielded 0.4162, 0.6528, and 0.7943 for 10, 20, and 30-s intervals, respectively. These findings demonstrate that using NNI counts for ultra-short-term stress prediction improves accuracy by accounting for individual bio-signal variations.

ConDiff-rPPG: Robust Remote Physiological Measurement to Heterogeneous Occlusions

Remote photoplethysmography (rPPG) is a contactless technique that facilitates the measurement of physiological signals and cardiac activities through facial video recordings. This approach holds tremendous potential for various applications. However, existing rPPG methods often did not account for different types of occlusions that commonly occur in real-world scenarios, such as temporary movement or actions of humans in videos or dust on camera. The failure to address these occlusions can compromise the accuracy of rPPG algorithms. To address this issue, we proposed a novel Condiff-rPPG to improve the robustness of rPPG measurement facing various occlusions. First, we compressed the damaged face video into a spatio-temporal representation with several types of masks. Second, the diffusion model was designed to recover the missing information with observed values as a condition. Moreover, a novel low-rank decomposition regularization was proposed to eliminate background noise and maximize informative features. ConDiff-rPPG ensured consistency in optimization goals during the training process. Through extensive experiments, including intra- and cross-dataset evaluations, as well as ablation tests, we demonstrated the robustness and generalization ability of our proposed model.

Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features

Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.

Heart Rate Estimation from Facial Image Sequences of a Dual-Modality RGB-NIR Camera

This paper presents an RGB-NIR (Near Infrared) dual-modality technique to analyze the remote photoplethysmogram (rPPG) signal and hence estimate the heart rate (in beats per minute), from a facial image sequence. Our main innovative contribution is the introduction of several denoising techniques such as Modified Amplitude Selective Filtering (MASF), Wavelet Decomposition (WD), and Robust Principal Component Analysis (RPCA), which take advantage of RGB and NIR band characteristics to uncover the rPPG signals effectively through this Independent Component Analysis (ICA)-based algorithm. Two datasets, of which one is the public PURE dataset and the other is the CCUHR dataset built with a popular Intel RealSense D435 RGB-D camera, are adopted in our experiments. Facial video sequences in the two datasets are diverse in nature with normal brightness, under-illumination (i.e., dark), and facial motion. Experimental results show that the proposed method has reached competitive accuracies among the state-of-the-art methods even at a shorter video length. For example, our method achieves MAE = 4.45 bpm (beats per minute) and RMSE = 6.18 bpm for RGB-NIR videos of 10 and 20 s in the CCUHR dataset and MAE = 3.24 bpm and RMSE = 4.1 bpm for RGB videos of 60-s in the PURE dataset. Our system has the advantages of accessible and affordable hardware, simple and fast computations, and wide realistic applications.

Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring

Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman's correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics.

Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal

Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%.