Pulsatile Signal Research Articles

Transient frequency preference responses in cell signaling systems

Ligand–receptor systems, covalent modification cycles, and transcriptional networks are the fundamental components of cell signaling and gene expression systems. While their behavior in reaching a steady-state regime under step-like stimulation is well understood, their response under repetitive stimulation, particularly at early time stages is poorly characterized. Yet, early-stage responses to external inputs are arguably as informative as late-stage ones. In simple systems, a periodic stimulation elicits an initial transient response, followed by periodic behavior. Transient responses are relevant when the stimulation has a limited time span, or when the stimulated component’s timescale is slow as compared to the timescales of the downstream processes, in which case the latter processes may be capturing only those transients. In this study, we analyze the frequency response of simple motifs at different time stages. We use dose-conserved pulsatile input signals and consider different metrics versus frequency curves. We show that in ligand–receptor systems, there is a frequency preference response in some specific metrics during the transient stages, which is not present in the periodic regime. We suggest this is a general system-level mechanism that cells may use to filter input signals that have consequences for higher order circuits. In addition, we evaluate how the described behavior in isolated motifs is reflected in similar types of responses in cascades and pathways of which they are a part. Our studies suggest that transient frequency preferences are important dynamic features of cell signaling and gene expression systems, which have been overlooked.

Vascular Aging Entails Arterial Dilatation in Addition to Increased Arterial Stiffness

Introduction: The increase in arterial blood pressure occurring with aging is considered an important risk factor for end-organ damage in, among others, the brain. How exactly this risk emerges is largely unknown. Methods: We investigated the effect of aging on middle cerebral flow velocity (MCAFV) and noninvasive arterial blood pressure (NIBP) in 105 healthy volunteers aged 18 to 82 years. Both pulsatile signals were analyzed determining the first (Sys1) and second systolic peak (Sys2) and the diastolic flow velocity 560 ms after stroke onset (D560). Results: For NIBP, a linear increase was found with aging for Sys2 and D560 (0.72 and 0.14 mm Hg/year, respectively) but not for Sys1. For MCAFV, a linear decrease was found with aging for all variables, being more rapid for Sys1 than for Sys2 or D560 (−0.94, −0.41, and −0.54 cm/s/year, respectively). Both signals change from Sys1 dominant in young adults to increasingly Sys2 dominant in the elderly. Discussion: That MCAFV decreases with aging, whereas NIBP increases can best be explained by assuming arterial dilatation. Apart from the generally accepted increase in arterial stiffness, arterial dilatation may be an important additional factor in vascular aging: the loss of elastin allows arteries to dilate until restricted by arterial collagen. Conclusion: Normal values collected in this study support protocols for non-invasive monitoring that require a correction for age.

Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information that can offer insights into neurofluid dynamics, vascular health, and waste clearance function. The availability of cerebral vessel segmentation could facilitate fluid dynamics research in fMRI. However, without magnetic resonance angiography scans, cerebral vessel segmentation is challenging and time-consuming. This study leverages cardiac-induced pulsatile fMRI signal to develop a data-driven, automatic segmentation of large cerebral arteries and the superior sagittal sinus (SSS). The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) aging dataset, the method's reproducibility was tested on 422 participants aged 36 to 100 years, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that the large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating the investigation of fluid dynamics in these regions.

PPG-based Vital Signs Measurement Using SmartPhone Camera: Review

Due to their growing popularity and rapid performance improvements, smartphones have the potential to be an accurate, low-cost physiological monitoring tool that is useful outside of the clinical setting. Since the cardiac pulse is responsible for the slight color changes in skin, a pulsatile signal that is also known as a photoplethysmographic (PPG) signal can be evaluated by utilizing a digital camera to record a video of your Region Of Interest (ROI). This review Study focuses on several research works that have used PPG-based video smartphone camera methods to measure Heart Rate (HR), Blood Pressure (BP), Oxygen Saturation (SpO2), and Respiratory Rate (RR). Comparing the approaches and results of each study (HR measurement studies produced acceptable findings with error rates less than 5%, BP error rates of results less than 10%, and SpO2 =<3.5%, by comparing the predicted models reading with the standard references reading) shows how smartphone-based physiological monitoring might be used in medical settings.

Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor.

Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.

FLUJO PULSÁTIL DE FLUIDOS VISCOELÁSTICOS EN TUBOS

In this study, we present an experimental investigation of the flow structure changes in non-Newtonian fluids subjected to periodic variable fluxes inside rigid-walled tubes. We employ a liquid mixture composed of water and polyacrylamide to account for various rheological properties. We obtain the velocity fields for each experimental case using the Particle Image Velocimetry (PIV) technique and we analyse their variations based on different properties of the pulsatile input signal. Reynolds numbers between 200 and 350 are considered, with a 10% variation in amplitude.

Measuring pulsatile cortical blood flow and volume during carotid endarterectomy.

Carotid endarterectomy (CEA) involves removal of plaque in the carotid artery to reduce the risk of stroke and improve cerebral perfusion. This study aimed to investigate the utility of assessing pulsatile blood volume and flow during CEA. Using a combined near-infrared spectroscopy/diffuse correlation spectroscopy instrument, pulsatile hemodynamics were assessed in 12 patients undergoing CEA. Alterations to pulsatile amplitude, pulse transit time, and beat morphology were observed in measurements ipsilateral to the surgical side. The additional information provided through analysis of pulsatile hemodynamic signals has the potential to enable the discovery of non-invasive biomarkers related to cortical perfusion.

Pulsatile microvascular cerebral blood flow waveforms change with intracranial compliance and age.

Diffuse correlation spectroscopy (DCS) is an optical method to measure relative changes in cerebral blood flow (rCBF) in the microvasculature. Each heartbeat generates a pulsatile signal with distinct morphological features that we hypothesized to be related to intracranial compliance (ICC). We aim to study how three features of the pulsatile rCBF waveforms: the augmentation index (AIx), the pulsatility index, and the area under the curve, change with respect to ICC. We describe ICC as a combination of vascular compliance and extravascular compliance. Since patients with Chiari malformations (CM) () have been shown to have altered extravascular compliance, we compare the morphology of rCBF waveforms in CM patients with age-matched healthy control (). AIx measured in the supine position was significantly less in patients with CM compared to healthy controls (). Since physiologic aging also leads to changes in vessel stiffness and intravascular compliance, we evaluate how the rCBF waveform changes with respect to age and find that the AIx feature was strongly correlated with age (, , and , ). These results suggest that the AIx measured in the cerebral microvasculature using DCS may be correlated to changes in ICC.

BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest.

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as "physiological noise" and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.

Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications.

Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.

A STUDY OF PERFUSION INDEX IN PRETERM AND TERM BABIES

Background and objectives: PI is a numeric value that is obtained from the ratio between pulsatile signal, set by arterial blood flow, and non pulsatile signal generated by skin, venous blood flow, and other local issues. Perfusion index is easily applicable, non-invasive, and continuous parameter that reflects changes in the cardiac output and vasomotor tone. Objectives: 1) To establish baseline PI values in healthy term and preterm infants . 2) Correlation of PI with birth weight . 3) Comparison of PI value for normal and sick newborns in both term and preterm. Methodology: This is a prospective observational study done between August 2022 to August 2023 (12 months),with sample size of 69 . Median PI value are obtained from the average of PI values recorded at 6-second intervals for a minimum of 2 minutes, 2 times a day for a duration of 3 days.Thus PI values are obtained in both sick term and preterm babies were compared with the PI values obtained in healthy term and preterm. Results: In the present study, the mean PI was 1.32 ± 0.82 with a range of 1.12 - 1.52. The mean perfusion index among preterm was 0.98 ±0.49 and the term was 1.52 ±0.91. The perfusion index was greater among term newborns compared to preterm with statistical significance. (P=0.007) Conclusion: PI values differed according to gestational age and to the presence of comorbidities, confirming that it might represent a valuable tool in the early identification of adverse outcomes in neonates.

Enhancing diffuse correlation spectroscopy pulsatile cerebral blood flow signal with near-infrared spectroscopy photoplethysmography.

Combining near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) allows for quantifying cerebral blood volume, flow, and oxygenation changes continuously and non-invasively. As recently shown, the DCS pulsatile cerebral blood flow index () can be used to quantify critical closing pressure (CrCP) and cerebrovascular resistance (). Although current DCS technology allows for reliable monitoring of the slow hemodynamic changes, resolving pulsatile blood flow at large source-detector separations, which is needed to ensure cerebral sensitivity, is challenging because of its low signal-to-noise ratio (SNR). Cardiac-gated averaging of several arterial pulse cycles is required to obtain a meaningful waveform. Taking advantage of the high SNR of NIRS, we demonstrate a method that uses the NIRS photoplethysmography (NIRS-PPG) pulsatile signal to model DCS , reducing the coefficient of variation of the recovered pulsatile waveform () and allowing for an unprecedented temporal resolution (266Hz) at a large source-detector separation (). In 10 healthy subjects, we verified the quality of the NIRS-PPG during common tasks, showing high fidelity against ( ). We recovered CrCP and at 0.25Hz, times faster than previously achieved with DCS. NIRS-PPG improves DCS SNR, reducing the number of gate-averaged heartbeats required to recover CrCP and .

Non-invasive monitoring of intracranial pressure changes: healthy volunteers study.

Objective: This research aims to evaluate the possible association between pulsatile near infrared spectroscopic waveform features and induced changes in intracranial pressure in healthy volunteers. Methods: An optical intracranial pressure sensor was attached to the forehead of 16 healthy volunteers. Pulsatile near infrared spectroscopic signals were acquired from the forehead during body position changes and Valsalva manoeuvers. Features were extracted from the pulsatile signals and analyses were carried out to investigate the presence of statistical differences in the features when intracranial pressure changes were induced. Classification models were developed utilizing the features extracted from the pulsatile near-infrared spectroscopic signals to classify between different body positions and Valsalva manoeuvre. Results: The presence of significant differences in the majority of the analyzed features (p 0.05) indicates the technique's ability to distinguish between variations in intracranial pressure. Furthermore, the disparities observed in the optical signal features captured by the proximal and distal photodetectors support the hypothesis that alterations in back-scattered light directly correspond to brain-related changes. Further research is required to subtract distal and proximal signals and construct predictive models employing a gold standard measurement for non-invasive, continuous monitoring of intracranial pressure. Conclusion: The study investigated the use of pulsatile near infrared spectroscopic signals to detect changes in intracranial pressure in healthy volunteers. The results revealed significant differences in the features extracted from these signals, demonstrating a correlation with ICP changes induced by positional changes and Valsalva manoeuvre. Classification models were capable of identifying changes in ICP using features from optical signals from the brain, with a sensitivity ranging from 63.07% to 80% and specificity ranging from 60.23% to 70% respectively. These findings underscored the potential of these features to effectively identify alterations in ICP. Significance: The study's results demonstrate the feasibility of using features extracted from optical signals from the brain to detect changes in ICP induced by positional changes and Valsalva manoeuvre in healthy volunteers. This represents a first step towards the non-invasive monitoring of intracranial pressure.

Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

Robustness of physiological parameters obtained from photoplethysmographic (PPG) signals is highly dependent on a signal quality that is often affected by the motion artefacts (MAs) generated during physical activity. This study aims to suppress MAs and obtain reliable physiological readings using the part of the pulsatile signal, captured by a multi-wavelength illumination optoelectronic patch sensor (mOEPS), that minimizes the residual between the measured signal and the motion estimates obtained from an accelerometer. The minimum residual (MR) method requires the simultaneous collection of (1) multiple wavelength data from the mOEPS, and (2) motion reference signals from a triaxial accelerometer attached to the mOEPS. The MR method suppress those frequencies associated with motion in a manner that is easily embedded on a microprocessor. The performance of the method in reducing both in-band and out-of-band frequencies of MAs is evaluated through two protocols with 34 subjects engaged in the study. The MA-suppressed PPG signal, obtained through MR, enables the calculation of the heart rate (HR) with an average absolute error of 1.47 beats/min for the IEEE-SPC datasets, and the calculation of HR and respiration rate (RR) to 1.44 beats/min and 2.85 breaths/min respectively for our in-house datasets. Oxygen saturation (SpO 2) levels calculated from the minimum residual waveform are consistent with the expected values of ≥ 95%. The comparison with the reference HR and RR show errors with an absolute accuracy of and the Pearson correlation ( R) for HR and RR are 0.9976 and 0.9118 respectively. These outcomes demonstrate that MR is capable of effective suppression of MAs for a range of physical activity intensities and to achieve real-time signal processing for wearable health monitoring.

PO-04-214 VIDEO-BASED FACIAL DETECTION OF ATRIAL FIBRILLATION ACROSS HUMAN COMPLEXIONS

It has been posited that the Black vs. White disparity in stroke risk may be partly attributable to the relative underdetection of AF in Black compared with White patients. We propose a novel monitoring solution that could minimize the impact of the socio-economic disparities in access to remote monitoring solutions. We developed a video-based monitoring technology to detect AF without the need for a patient to adopt a dedicated wearable device. It is a low-cost software-only solution running on smartphones. It eliminates requirements for patient compliance with recording procedures since it passively monitors patients while they are using their smartphones for other purposes. The technology captures a pulsatile signal from patients' faces. Remote video-based cardiac monitoring measures the modulation of ambient light reflected from one’s face due to changes in blood volume (hemoglobin) in the upper layers of the skin. In addition to hemoglobin, melanin is also a chromophore of the human skin. Hence, we aim to measure the accuracy of the technology to measure heart rate (HR) and its performance to detect the presence of AF across the whole spectrum of human complexions. We enrolled patients with paroxysmal and persistent AF. We defined 3 complexion groups using the Fitzpatrick score: white (A), tanned and olive-color (B), brown and black (C). Measurements were conducted under 4 indoor illumination levels: 50, 100, 200, and 500 lux. For reference, we used a single-lead EKG. We used 25% of the cohort to train the technology. HR accuracy was assessed using the Bland-Altman method with paired replicate measures based on one-way repeated ANOVA. The detection performance was measured as average sensitivity (SENS) and specificity (SPEC) adjusted for repeated measures. We collected 7,042 recordings from 60 AF patients (67±10 yrs, 44 men). We enrolled 23, 26, and 11 patients in the complexion groups A, B, and C, respectively. In the validation, 87% of the recordings were conclusive. Overall SENS and SPEC were 96% and 92%. For an illumination >50 lux, the SENS and SPEC for the complexion groups were: [A: 99%,94%], [B: 91%,93%], [C: 96%,90%]. The mean HR measurement errors in reference to the EKGs were: [A: -2.0 bpm], [B: -0.2 bpm], and [C: 0.3 bpm] with limits of agreements <5 bpm (upper to lower bound intervals). The proposed monitoring technology enables an accurate measure of HR and the detection of AF across all human skin complexions and under indoor illumination.

Head Phantom for the Acquisition of Pulsatile Optical Signals for Traumatic Brain Injury Monitoring

(1) Background: Tissue phantoms can provide a rigorous, reproducible and convenient approach to evaluating an optical sensor’s performance. The development, characterisation and evaluation of a vascular head/brain phantom is described in this study. (2) Methods: The methodology includes the development of mould-cast and 3D-printed anatomical models of the brain and the skull and a custom-made in vitro blood circulatory system used to emulate haemodynamic changes in the brain. The optical properties of the developed phantom were compared to literature values. Artificial cerebrospinal fluid was also incorporated to induce changes in intracranial pressure. (3) Results: A novel head model was successfully developed to mimic the brain and skull anatomies and their optical properties within the near-infrared range (660–900 nm). The circulatory system developed mimicked normal arterial blood pressure values, with a mean systole of 118 ± 8.5 mmHg and diastole of 70 ± 8.5 mmHg. Similarly, the cerebrospinal fluid circulation allowed controlled intracranial pressure changes from 5 to 30 mmHg. Multiwavelength pulsatile optical signals (photoplethysmograms (PPGs)) from the phantom’s cerebral arteries were successfully acquired. Conclusions: This unique head phantom technology forms the basis of a novel research tool for investigating the relationship between cerebral pulsatile optical signals and changes in intracranial pressure and brain haemodynamics.

A Non-Invasive Optical Multimodal Photoplethysmography-Near Infrared Spectroscopy Sensor for Measuring Intracranial Pressure and Cerebral Oxygenation in Traumatic Brain Injury

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients.

Bio-Inspired Vision and Neuromorphic Image Processing Using Printable Metal Oxide Photonic Synapses

We propose a neuromorphic vision system that uses digitally printed metal oxide photonic synapses with optical tunability and temporally correlated plasticity. The neuromorphic vision system provides the first demonstration of encoding ambient light intensity in the time domain and captures optical images by encoding their pixel intensity into pulsatile signals in real time, analogous to a biological visual retina and optic nerve. The system can then process the information and form memory, emulating the image data processing in the human brain. The system can realize image-preprocessing functions to increase image contrast and reduce image background noise, thereby effectively improving the classification and recognition accuracy. Dynamic stimulation of visual cortical prosthetics shows that the system is capable of dynamic perception and memory to detect motion and mimic coherent visual perception. Furthermore, the photonic synapses with a two-terminal structure using printed semiconducting fiber arrays could also facilitate large-scale integration. The proposed system offers the potential to simplify neuromorphic visual circuits and may have applications in autonomous smart devices, such as driverless cars, smart surveillance systems, and intelligent healthcare.

Spectral analysis for pulse rate variability assessment from simulated photoplethysmographic signals.

Introduction: Pulse rate variability (PRV) refers to the changes in pulse rate through time and is extracted from pulsatile signals such as the photoplethysmogram (PPG). Although PRV has been used as a surrogate of heart rate variability (HRV), which is measured from the electrocardiogram (ECG), these variables have been shown to have differences, and it has been hypothesised that these differences may arise from technical aspects that may affect the reliable extraction of PRV from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals. Aim: In this study, the extraction of frequency-domain information from PRV was studied, in order to establish the best performing combination of parameters and algorithms to obtain the spectral representation of PRV. Methods: PPG signals with varying and known PRV content were simulated, and PRV information was extracted from these signals. Several spectral analysis techniques with different parameters were applied, and absolute, relative and centroid-related frequency-domain indices extracted from each combination. Indices from extracted and known PRV were compared using factorial analyses and Kruskal-Wallis tests to determine which spectral analysis technique gave the best performing results. Results: It was found that using fast Fourier transform and the multiple signal classification (PMUSIC) algorithms gave the best results, combined with cubic spline interpolation and a frequency resolution of 0.0078Hz for the former; and a linear interpolation with a frequency resolution as low as 1.22 × 10-4, as well as applying a fifth order model, for the latter. Discussion: Considering the lower complexity of FFT over PMUSIC, FFT should be considered as the appropriate technique to extract frequency-domain information from PRV signals.

Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms

Pulse rate variability (PRV) describes the changes in pulse rate through time, when measured using pulsatile signals such as photoplethysmograms (PPG). PRV has been used as a surrogate of heart rate variability (HRV), but their relationship is not straightforward, both due to physiological differences and to effects of technical aspects on the extraction of PRV information from pulsatile signals such as the PPG. One of the factors that may affect PRV analysis is the presence of noise and the filtering strategy used to pre-process the PPG signal. In this study, the aim was to evaluate the best performing filtering strategy for the extraction of PRV information reliably from noise-contaminated synthetic PPG signals. Time domain, frequency domain and Poincaré plot indices were extracted from PRV trends obtained from the filtered PPG signals and compared against indices measured from a gold-standard simulated PRV function. It was found that PRV information can be reliably extracted from PPG signals filtered using lower low cutoff frequencies and elliptic IIR or equiripple or Parks–McClellan FIR filters, however the filtering parameters depend on the type of noise present in the signal. Moreover, special care should be taken to assess the pNN50 index from contaminated PPG signals, regardless of the type of noise. Future studies should aim to validate these results from real PPG data.