Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.

Vascular ageing is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

A pilot clinical study to estimate intracranial pressure utilising cerebral photoplethysmograms in traumatic brain injury patients.

PURPOSE: In this research, a non-invasive intracranial pressure (nICP) optical sensor was developed and evaluated in a clinical pilot study. The technology relied on infrared light to probe brain tissue, using photodetectors to capture backscattered light modulated by vascular pulsations within the brain's vascular tissue. The underlying hypothesis was that changes in extramural arterial pressure could affect the morphology of recorded optical signals (photoplethysmograms, or PPGs), and analysing these signals with a custom algorithm could enable the non-invasive calculation of intracranial pressure (nICP). METHODS: This pilot study was the first to evaluate the nICP probe alongside invasive ICP monitoring as a gold standard. nICP monitoring occurred in 40 patients undergoing invasive ICP monitoring, with data randomly split for machine learning. Quality PPG signals were extracted and analysed for time-based features. The study employed Bland-Altman analysis and ROC curve calculations to assess nICP accuracy compared to invasive ICP data. RESULTS: Successful acquisition of cerebral PPG signals from traumatic brain injury (TBI) patients allowed for the development of a bagging tree model to estimate nICP non-invasively. The nICP estimation exhibited 95% limits of agreement of 3.8 mmHg with minimal bias and a correlation of 0.8254 with invasive ICP monitoring. ROC curve analysis showed strong diagnostic capability with 80% sensitivity and 89% specificity. CONCLUSION: The clinical evaluation of this innovative optical nICP sensor revealed its ability to estimate ICP non-invasively with acceptable and clinically useful accuracy. This breakthrough opens the door to further technological refinement and larger-scale clinical studies in the future. TRIAL REGISTRATION: NCT05632302, 11th November 2022, retrospectively registered.

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.

A review of the effect of skin pigmentation on pulse oximeter accuracy.

Objective. Pulse oximetry is a non-invasive optical technique used to measure arterial oxygen saturation (SpO2) in a variety of clinical settings and scenarios. Despite being one the most significant technological advances in health monitoring over the last few decades, there have been reports on its various limitations. Recently due to the Covid-19 pandemic, questions about pulse oximeter technology and its accuracy when used in people with different skin pigmentation have resurfaced, and are to be addressed.Approach. This review presents an introduction to the technique of pulse oximetry including its basic principle of operation, technology, and limitations, with a more in depth focus on skin pigmentation. Relevant literature relating to the performance and accuracy of pulse oximeters in populations with different skin pigmentation are evaluated.Main Results. The majority of the evidence suggests that the accuracy of pulse oximetry differs in subjects of different skin pigmentations to a level that requires particular attention, with decreased accuracy in patients with dark skin.Significance. Some recommendations, both from the literature and contributions from the authors, suggest how future work could address these inaccuracies to potentially improve clinical outcomes. These include the objective quantification of skin pigmentation to replace currently used qualitative methods, and computational modelling for predicting calibration algorithms based on skin colour.

Optical determination of lithium therapeutic levels in micro-volumes of interstitial fluid.

BACKGROUND: Long-term management of bipolar disorder (BD), characterized by mood fluctuating between episodes of mania and depression, involves the regular taking of lithium preparations as the most reliable mood stabilizer for bipolar patients. However, despite its effectiveness in preventing and reducing mood swings and suicidality, lithium has a very narrow therapeutic index and it is crucial to carefully monitor lithium plasma levels as concentrations >1.2 mmol/L are potentially toxic and can be fatal. Current methods of lithium therapeutic monitoring involve frequent blood tests, which have several drawbacks related to the invasiveness of the technique, comfort, cost and reliability. Dermal interstitial fluid (ISF) is an accessible and information-rich biofluid, and correlations have been found between blood and ISF levels of lithium medication. METHODS: In the current study, we sought to investigate the optical determination of lithium therapeutic concentrations in samples of ISF extracted from porcine skin utilizing a microneedle-based approach. Monitoring of lithium levels in porcine ISF was achieved by employing a spectrophotometric method based on the reaction between the chromogenic agent Quinizarin and lithium. RESULTS: The resulting spectra show spectral variations which relate to lithium concentrations of lithium in samples of porcine ISF with a coefficient of determination (R2 ) of 0.9. This study has demonstrated successfully that therapeutic levels of lithium in micro-volumes of porcine ISF can be measured with a high level of accuracy utilizing spectroscopic techniques. CONCLUSIONS: The results support the future development of a miniaturized and minimally-invasive device for lithium monitoring in bipolar patients.

Monte Carlo Simulation of the Effect of Human Skin Melanin in Light-Tissue Interactions.

Recent reports have highlighted the potential challenges skin pigmentation can have in the accurate estimation of arterial oxygen saturation when using a pulse oximeter. Pulse oximeters work on the principle of photoplethysmography (PPG), an optical technique used for the assessment of volumetric changes in vascular tissue. The primary aim of this research is to investigate the effect of melanin on tissue when utilising the technique of PPG. To address this, a Monte Carlo (MC) light-tissue interaction model is presented to explore the behaviour of melanin in the visible range in the epidermis. A key novelty in this paper is the ability to model the Modified Beer Lambert Law (MBLL) through a fully functional three-dimensional (3D) model in reflective optical geometry. Maximum photon penetration depth was achieved by red light, however limited bio-optical information was retrieved by moderately and darkly pigmented skin at source-detector separations of less than 3 mm. The current MC model can be modified to provide a more realistic representation of absorption and scattering processes in skin.

Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals.

Pulse rate variability (PRV) has been proposed as a surrogate for the estimation of Heart Rate Variability (HRV), which is a non-invasive technique used to assess the cardiac autonomic activity. However, both physiological and technical factors may affect the relationship between HRV and PRV, and there are no standards for the analysis of PRV from photoplethysmographic (PPG) signals. The aim of this study was to determine the best outlier management strategies for PRV analysis. 117 PPG signals with randomly generated PRV information were simulated using Gaussian signals. From these, interbeat intervals were detected and different outlier detection and correction techniques were applied. Time and frequency-domain and non-linear PRV indices were extracted and compared with respect to the gold standard values obtained from the simulated PRV information. The results show that, in good quality PPG signals, there is no need to apply any outlier management technique for the extraction of PRV information. Clinical relevance- Establishing guidelines for PRV mea-surement can lead to more reliable and comparable results, as well as to the increase in the use of this variable for the diagnosis and monitoring of cardiovascular and autonomic conditions.

Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography.

OBJECTIVE: Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content. METHODS: PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests. RESULTS: From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz. CONCLUSION: The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application. SIGNIFICANCE: The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.

Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features.

Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches. Although, good performance has been reported in the literature, it was mainly achieved on a small inadequate sample size using conventional models that are unable to account for the temporal variations in the input vector. To address these limitations, this paper proposes cuff-less and continuous blood pressure estimation using Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU). The models were evaluated on 942 patients acquired from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset. The proposed models produced superior results in comparison with conventional artificial neural network. In particular, the best performance was achieved by the GRU, with mean absolute error and standard deviation of 5.77 ± 8.52 mmHg and 3.33±5.02 mmHg for systolic (SBP) and diastolic blood pressure (DBP), respectively. Furthermore, the results comply with the international standards for cuff-less blood pressure estimation.

Effect of Filtering of Photoplethysmography Signals in Pulse Rate Variability Analysis.

Due to the widespread use and simplicity of photoplethysmography (PPG) signals, and because this signal contains information related to pulse rate, several studies have started to propose the use of Pulse Rate Variability (PRV) for the assessment of cardiovascular autonomic nervous activity, instead of using Heart Rate Variability (HRV) obtained with the electrocardiogram (ECG). However, there is a lack of standardisation and guidelines for the measurement of PRV from PPG signals, which might hinder comparability among studies and validation of results. The aim of this study was to evaluate different digital filters on PPG signals and their effects on PRV information, compared to HRV obtained from ECG. PPG and ECG signals obtained from healthy volunteers were used to measure HRV and PRV. PPG signals were filtered using different FIR and IIR digital filters, with several cut-off frequencies. The results indicate that filtering PPG signals using IIR filters and lower low-cut-off frequencies allow for the acquisition of more reliable PRV information, with lower Bland-Altman ratios and higher cross-correlations when compared to HRV. This is a first step in establishing guidelines and standards for the analysis of PRV information using PPG signals.Clinical relevance- Pulse rate variability might be a useful tool for the assessment of the cardiovascular autonomic nervous system. This study is the first step for establishing standards of measurement of this signal, which helps in the comparability and validation of the technique.

In-Vitro Investigation of Flow Profiles in Arteries Using the Photoplethysmograph.

The flow profile in the artery reflects the health status of the vessel and generally the arterial system. The aim of this pilot study was to investigate in-vitro the effect of flow profiles on reflective photoplethysmography (PPG) signals at different steady state flow rates and levels of vessel constrictions. A simplified model of an arterial system was built, consisting of a steady state flow gear pump, PVC vinyl tubing, reservoir and a clamp with a micrometer gauge. The blood mimicking fluid (2.5% India ink and water solution) was pumped through the model. It was found that the waveforms of the PPG signals fluctuate irregularly and the magnitude of the frequency components was increased below 60 Hz in cases of turbulent flow (Re = 2503). These preliminary results suggest that PPG could be the basis for new technologies for assessing the profile of the blood flow in the artery. Future studies have to be carried out with pulsatile flow and more complex models that are more similar to the human arterial system.Clinical Relevance- The PPG signal reflects changes in the flow profile caused by the stenotic rigid vessel.

Classification of blood pressure in critically ill patients using photoplethysmography and machine learning.

OBJECTIVE: The aim of this study was to evaluate the capability of features extracted from photoplethysmography (PPG) based Pulse Rate Variability (PRV) to classify hypertensive, normotensive and hypotensive events, and to estimate mean arterial, systolic and diastolic blood pressure in critically ill patients. METHODS: Time-domain, frequency-domain and non-linear indices from PRV were extracted from 5-min and 1-min segments obtained from PPG signals. These features were filtered using machine learning algorithms in order to obtain the optimal combination for the classification of hypertensive, hypotensive and normotensive events, and for the estimation of blood pressure. RESULTS: 5-min segments allowed for an improved performance in both classification and estimation tasks. Classification of blood pressure states showed around 70% accuracy and around 75% specificity. The sensitivity, precision and F1 scores were around 50%. In estimating mean arterial, systolic, and diastolic blood pressure, mean absolute errors as low as 2.55 ± 0.78 mmHg, 4.74 ± 2.33 mmHg, and 1.78 ± 0.14 mmHg were obtained, respectively. Bland-Altman analysis and Wilcoxon rank sum tests showed good agreement between real and estimated values, especially for mean and diastolic arterial blood pressures. CONCLUSION: PRV-based features could be used for the classification of blood pressure states and the estimation of blood pressure values, although including additional features from the PPG waveform could improve the results. SIGNIFICANCE: PRV contains information related to blood pressure, which may aid in the continuous, noninvasive, non-intrusive estimation of blood pressure and detection of hypertensive and hypotensive events in critically ill subjects.

Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients.

Heart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland-Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal-Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection.

Near Infrared (800-2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared. Results showed significant improvement in predicting analyte concentration when analysis was performed on full range spectral data. This is in comparison to analysis of limited spectral regions or lactate signature peaks, which yielded poorer prediction models. Furthermore, for the same region, FTNIR showed 10% better predictive capability than the dual beam dispersive NIR spectrometer.

Design and Analysis of a Continuous and Non-Invasive Multi-Wavelength Optical Sensor for Measurement of Dermal Water Content.

Dermal water content is an important biophysical parameter in preserving skin integrity and preventing skin damage. Traditional electrical-based and open-chamber evaporimeters have several well-known limitations. In particular, such devices are costly, sizeable, and only provide arbitrary outputs. They also do not permit continuous and non-invasive monitoring of dermal water content, which can be beneficial for various consumer, clinical, and cosmetic purposes. We report here on the design and development of a digital multi-wavelength optical sensor that performs continuous and non-invasive measurement of dermal water content. In silico investigation on porcine skin was carried out using the Monte Carlo modeling strategy to evaluate the feasibility and characterize the sensor. Subsequently, an in vitro experiment was carried out to evaluate the performance of the sensor and benchmark its accuracy against a high-end, broad band spectrophotometer. Reference measurements were made against gravimetric analysis. The results demonstrate that the developed sensor can deliver accurate, continuous, and non-invasive measurement of skin hydration through measurement of dermal water content. Remarkably, the novel design of the sensor exceeded the performance of the high-end spectrophotometer due to the important denoising effects of temporal averaging. The authors believe, in addition to wellbeing and skin health monitoring, the designed sensor can particularly facilitate disease management in patients presenting diabetes mellitus, hypothyroidism, malnutrition, and atopic dermatitis.

Near-Infrared Spectroscopy (NIRS) in Traumatic Brain Injury (TBI).

Traumatic brain injury (TBI) occurs when a sudden trauma causes damage to the brain. TBI can result when the head suddenly and violently impacts an object or when an object pierces the skull and enters brain tissue. Secondary injuries after traumatic brain injury (TBI) can lead to impairments on cerebral oxygenation and autoregulation. Considering that secondary brain injuries often take place within the first hours after the trauma, noninvasive monitoring might be helpful in providing early information on the brain's condition. Near-infrared spectroscopy (NIRS) is an emerging noninvasive monitoring modality based on chromophore absorption of infrared light with the capability of monitoring perfusion of the brain. This review investigates the main applications of NIRS in TBI monitoring and presents a thorough revision of those applications on oxygenation and autoregulation monitoring. Databases such as PubMed, EMBASE, Web of Science, Scopus, and Cochrane library were utilized in identifying 72 publications spanning between 1977 and 2020 which were directly relevant to this review. The majority of the evidence found used NIRS for diagnosis applications, especially in oxygenation and autoregulation monitoring (59%). It was not surprising that nearly all the patients were male adults with severe trauma who were monitored mostly with continue wave NIRS or spatially resolved spectroscopy NIRS and an invasive monitoring device. In general, a high proportion of the assessed papers have concluded that NIRS could be a potential noninvasive technique for assessing TBI, despite the various methodological and technological limitations of NIRS.

Heart Rate Variability and Multi-Site Pulse Rate Variability for the Assessment of Autonomic Responses to Whole-Body Cold Exposure.

Heart rate variability (HRV) is a noninvasive marker of cardiac autonomic activity and has been used in different circumstances to assess the autonomic responses of the body. Pulse rate variability (PRV), a similar variable obtained from pulse waves, has been used in recent years as a valid surrogate of HRV. However, the effect that localized changes in autonomic activity have in the relationship between HRV and PRV has not been entirely understood. In this study, a whole-body cold exposure protocol was performed to generate localized changes in autonomic activity, and HRV and PRV from different body sites were obtained. PRV measured from the earlobe and the finger was shown to differ from HRV, and the correlation between these variables was affected by the cold. Also, it was found that PRV from the finger was more affected by cold exposure than PRV from the earlobe. In conclusion, PRV is affected differently to HRV when localized changes in autonomic activity occur. Hence, PRV should not be considered as a valid surrogate of HRV under certain circumstances.Clinical Relevance- This indicates that pulse rate variability is affected differently to heart rate variability when autonomic activity is modified and suggests that pulse rate variability is not always a valid surrogate of heart rate variability.

Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks.

This paper proposes cuffless and continuous blood pressure estimation utilising Photoplethysmography (PPG) signals and state of the art recurrent network models, namely, Long Short Term Memory and Gated Recurrent Units. The models were validated on wide range of varying blood pressure and PPG signals acquired from the Multiparameter Intelligent Monitoring in Intensive Care database. Many features were extracted from the PPG waveform and several machine learning techniques were employed in an attempt to eliminate collinearity and reduce the size of input feature vector. Consequently, the most effective features for blood pressure estimation were selected. Experimental results show that the accuracy of the proposed methods outperform traditional models applied in the literature. The results satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.

Fabricating Novel PDMS Vessels for Phantoms in Photoplethysmography Investigations.

This paper introduces a novel technique for the development of custom polydimethylsiloxane (PDMS) vessels for use in phantom technologies. The method involves continuous dip coating of commercial silicone tubes with rapid curation in a single controlled process. The technique accommodates the production of different vessel diameters, wall thicknesses (56 µm-80 µm) and mechanical properties. Clear phantoms were fabricated to compare the commercial silicone tubes against the customs vessels. A pulsatile fluidic pump (BDCLabs, CO, USA) driven by a computer controlled linear motor generated the pulsatile flow through the phantom. The resulting flow profile, using the custom vessels, simulates human blood flow and the detected contact PPG signal from the phantom closely resembles the morphology of in vivo PPG waveforms with signal-to-noise ratios of 38.16 dB and 40.59 dB, compared to the closest commercially-available tubing at 5.38 dB and 10.59 dB for the red and infrared wavelengths respectively. The rigidity and thick walls of commercial silicone tubes impede the expansion of the tubing under systolic pressure. This technique eliminates this common limitation in phantom development.