Photoplethysmographic Waveform Research Articles

Full-Term and Preterm Newborns Differ more Significantly in Photoplethysmographic Waveform Variability than Heart Rate Variability

Background: Features of cardiovascular autonomic regulation in infants are poorly studied compared with adults. However, the clinical significance of autonomic dysfunction in infants is very high. The goal of our research was to study the temporal and frequency-dependent features, as well as low-frequency synchronization in cardiovascular autonomic regulation in full-term vs. preterm newborns, based on the analysis of their heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV). Methods: The study included three groups of newborns: 64 full-term newborns (with a gestational age at birth of 37–40 weeks) with a physiological course of the neonatal adaptation; 23 full-term newborns (with a gestational age at birth of 37–40 weeks) with a pathological course of the neonatal adaptation; and 17 preterm newborns (with a postconceptional age of 34 weeks or more). We conducted spectral analysis of HRV and PPGV, along with an assessment of the synchronization strength between low-frequency oscillations in HRV and in PPGV (synchronization index). We employed several options for the boundaries of the high-frequency (HF) band: 0.15–0.40 Hz, 0.2–2 Hz, 0.15–0.8 Hz, and 0.24–1.04 Hz. Results: Preterm newborns had higher heart rate, RMSSD, and PNN50 values relative to both groups of full-term newborns. Values of SDNN index and synchronization index (S index) were similar in all groups of newborns. Differences in frequency domain indices of HRV between groups of newborns depended on the considered options of HF band boundaries. Values of frequency domain indices of PPGV demonstrated similar differences between groups, regardless of the boundaries of considered options of HF bands and the location of PPG signal recording (forehead or leg). An increase in sympathetic influences on peripheral blood flow and a decrease in respiratory influences were observed along the following gradient: healthy full-term newborns → preterm newborns → full-term newborns with pathology. Conclusions: Differences in frequency domain indices of autonomic regulation between the studied groups of newborns depended on the boundaries of the considered options of the HF band. Frequency domain indices of PPGV revealed significantly more pronounced differences between groups of newborns than analogous HRV indicators. An increase in sympathetic influences on peripheral blood flow and a decrease in respiratory influences were observed along the following gradient: healthy full-term newborns → preterm newborns → full-term newborns with pathology.

Effect of transmural pressure on the estimation of arterial stiffness index from the photoplethysmographic waveform.

The aim of this study was to find the effect of transmural pressure on the determination of the photoplethysmographic (PPG) waveform arterial stiffness index (PPGAI). The study was conducted on 51 subjects without diagnosis of cardiovascular disease, aged between 24 and 74years. The relation between the transmural pressure, which is the difference between the arterial blood pressure and the PPG sensor contact pressure, and the PPGAI was determined. PPG, beat-to-beat blood pressure, and sensor contact pressure signals were recorded from the index, middle, and ring finger. The PPG sensor contact pressure of the index finger was increased from 20 to 120mmHg. The aortic augmentation index (AIx@75) was estimated with a SphygmoCor device as a reference. High correlation coefficients r = 0.79 and r = 0.83 between PPGAI and AIx@75, and low PPGAI standard deviations were observed at the transmural pressures of 10 and 20mmHg, respectively. Transmural pressure of 20mmHg can be considered suitable for the PPG signal registration and PPGAI calculation for the assessment of arterial stiffness. In summary, the contact pressure of the sensor should be selected according to theblood pressure of the subject finger in order to achieve the transmural pressure suitable for the assessment of PPGAI and arterial stiffness.

Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

Morphological features of the photoplethysmographic signal: a new approach to characterize the microcirculatory response to photobiomodulation.

Introduction and Objectives: Advanced analysis of the morphological features of the photoplethysmographic (PPG) waveform may provide greater understanding of mechanisms of action of photobiomodulation (PBM). Photobiomodulation is a non-ionizing, red to near-infrared irradiation shown to induce peripheral vasodilatation, promote wound healing, and reduce pain. Using laser Doppler flowmetry combined with thermal imaging we found previously in a clinical study that PBM stimulates microcirculatory blood flow and that baseline palm skin temperature determines, at least in part, why some individuals respond favorably to PBM while others do not. "Responders" (n = 12) had a skin temperature range of 33°C-37.5°C, while "non-responders" (n = 8) had "cold" or "hot" skin temperature (<33°C or >37.5°C respectively). The continuous PPG signals recorded from the index fingers of both hands in the original clinical study were subjected to advanced post-acquisitional analysis in the current study, aiming to identify morphological features that may improve the accuracy of discrimination between potential responders and non-responders to PBM. Methods: The PPG signals were detrended by subtracting the lower envelope from the raw signal. The Root Mean Square (RMS) and Entropy features were extracted as were two additional morphological features -- Smoothness and number of local extrema per PPG beat (#Extrema). These describe the signal jaggedness and were developed specifically for this study. The Wilcoxon test was used for paired comparisons. Correlations were determined by the Spearman correlation test (rs). Results: The PPG waveforms of responders to PBM had increased amplitude and decreased jaggedness (Baseline vs. 10' post-irradiation: Entropy, 5.0 ± 1.3 vs. 3.9 ± 1.1, p = 0.012; #Extrema, 4.0 ± 1.1 vs. 3.0 ± 1.6, p = 0.009; RMS, 1.6 ± 0.9 vs. 2.3 ± 1.2, p = 0.004; Smoothness, 0.10 ± 0.05 vs. 0.19 ± 0.16, p = 0.016). In addition, unilateral irradiation resulted in a bilateral response, although the response of the contralateral, non-irradiated hand was shorter in duration and lower in magnitude. Although subjects with 'cold,' or 'hot,' baseline skin temperature appeared to have morphologically distinct PPG waveforms, representing vasoconstriction and vasodilatation, these were not affected by PBM irradiation. Conclusion: This pilot study indicates that post-acquisitional analysis of morphological features of the PPG waveform provides new measures for the exploration of microcirculation responsiveness to PBM.

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat's performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

Rapid extraction of respiratory waveforms from photoplethysmography: A deep corr-encoder approach

Much of the information related to breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our corr-encoder model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference — the Capnogram. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show that the model is capable of producing respiratory waveforms that approach the gold standard, while in turn producing state of the art respiratory rate estimates. We also show that when it comes to capturing more advanced respiratory waveform characteristics such as duty cycle, our model is for the most part unsuccessful. A suggested reason for this, in light of a previous study on in-ear PPG, is that the respiratory variations in finger-PPG are far weaker compared with other recording locations. Importantly, our model can perform these waveform estimates in a fraction of a millisecond, giving it the capacity to produce over 6 hours of respiratory waveforms in a single second. Moreover, we attempt to interpret the behaviour of the kernel weights within the model, showing that in part our model intuitively selects different breathing frequencies. The model proposed in this work could help to improve the usefulness of consumer PPG-based wearables for medical applications, where detailed respiratory information is required.

Using the ear photoplethysmographic waveform as an early indicator of central hypovolemia in healthy volunteers utilizing LBNP induced hypovolemia model

Objective. To study the photoplethysmographic (PPG) waveforms of different locations (ear and finger) during lower body negative pressure (LBNP) induced hypovolemia. Then, to determine whether the PPG waveform can be used to detect hypovolemia during the early stage of LBNP. Approach. 36 healthy volunteers were recruited for progressive LBNP induced hypovolemia, with an endpoint of −60 mmHg or development of hypoperfusion symptoms, whichever comes first. Subjects tolerating the entire protocol without symptoms were designated as high tolerance (HT), while symptomatic subjects were designated as low tolerance (LT). Subjects were monitored with an electrocardiogram, continuous noninvasive blood pressure monitor, and two pulse oximetry probes, one on the ear (Xhale) and one the finger (Nellcor). Stroke volume was measured non-invasively utilizing Non-Invasive Cardiac Output Monitor (NICOM, Cheetah Medical). The waveform morphology was analyzed using novel PPG waveforms indices, including phase hemodynamic index (PHI) and amplitude hemodyamaic index and were evaluated from the ear PPG and finger PPG at different LBNP stages. Main results. The PHI, particularly the phase relationship between the second harmonic and the fundamental component of the ear PPG denoted as during the early stage of LBNP (−15 mmHg) in the HT and LT groups is statistically significantly different (p value = 0.0033) with the area under curve 0.81 (CI: 0.616–0.926). The other indices are not significantly different. The 5 fold cross validation shows that during the early stage of LBNP (−15 mmHg) as the single index could predict the tolerance of the subject with the sensitivity, specificity, accuracy and F1 as 0.771 ± 0.192, 0.71 ± 0.107, 0.7 ± 0.1 and 0.771 ± 0.192 respectively. Significance. The ear’s PPG PHI which compares the phases of the fundamental and second harmonic has the potential to be used as an early predictor of central hypovolemia.

Satellite-Linked Remote Physiologic Monitoring During Simulated Rural Ground Ambulance and Rotor Wing Transports

Telemedicine and remote monitoring are rapidly gaining momentum in health care, including in-home and postdischarge monitoring. However, remote monitoring for telemedicine teams and receiving medical centers is yet to reach emergency medical services. When transporting a critically ill patient, continuous vital sign monitoring may be helpful to enhance receiving center collaboration and change care delivery before arrival when necessary. Our primary objective was to demonstrate successful transmission and reception of real-time physiologic summary data to a hospital base station during ambulance transport using a low-energy Bluetooth wireless wearable device linked to iridium satellite communications. We completed our proof-of-concept study on 2 simulated ground ambulance transports and 1 helicopter flight in rural areas surrounding Rochester, Minnesota. Each simulated transport used Mayo Clinic–developed wearable devices paired one-to-one with a physiologic communication kit. Electrocardiographic and photoplethysmographic waveforms were processed on the wearables every 5 minutes, with vital sign data generated and transmitted to an iridium satellite and relayed to a base station network–protected computer and mobile application with real-time display of summary and location. Data and geolocation were tracked in real time with a mobile application during the simulated transport, and summary data packets were saved on Mayo Clinic servers. During the 3 simulated rural transports, all (n=7; 100%) wearables successfully captured, recorded, and transmitted raw waveform physiologic signals. All devices transmitted successfully via the iridium satellite to the base station network–protected computer and mobile application. In all, 106 (69%) of 154 transmissions were successfully processed. Our project demonstrated successful remote monitoring of physiologic vital signs during transport in rural, low–cellular signal areas. The use of live remote monitoring should be explored to understand its utility in practice and outcomes.

Photoplethysmography waveform analysis for classification of vascular tone and arterial blood pressure: Study based on neural networks

To test whether a Shallow Neural Network (S-NN) can detect and classify vascular tone dependent changes in arterial blood pressure (ABP) by advanced photopletysmographic (PPG) waveform analysis. PPG and invasive ABP signals were recorded in 26 patients undergoing scheduled general surgery. We studied the occurrence of episodes of hypertension (systolic arterial pressure (SAP) >140 mmHg), normotension and hypotension (SAP < 90 mmHg). Vascular tone according to PPG was classified in two ways: 1) By visual inspection of changes in PPG waveform amplitude and dichrotic notch position; where Classes I-II represent vasoconstriction (notch placed >50% of PPG amplitude in small amplitude waves), Class III normal vascular tone (notch placed between 20-50% of PPG amplitude in normal waves) and Classes IV-V-VI vasodilation (notch <20% of PPG amplitude in large waves). 2) By an automated analysis, using S-NN trained and validated system that combines seven PPG derived parameters. The visual assessment was precise in detecting hypotension (sensitivity 91%, specificity 86% and accuracy 88%) and hypertension (sensitivity 93%, specificity 88% and accuracy 90%). Normotension presented as a visual Class III (III-III) (median and 1st-3rd quartiles), hypotension as a Class V (IV-VI) and hypertension as a Class II (I-III); all p < .0001. The automated S-NN performed well in classifying ABP conditions. The percentage of data with correct classification by S-ANN was 83% for normotension, 94% for hypotension, and 90% for hypertension. Changes in ABP were correctly classified automatically by S-NN analysis of the PPG waveform contour.

Simultaneous photoplethysmography and blood flow measurements towards the estimation of blood pressure using speckle contrast optical spectroscopy.

Non-invasive continuous blood pressure monitoring remains elusive. There has been extensive research using the photoplethysmographic (PPG) waveform for blood pressure estimation, but improvements in accuracy are still needed before clinical use. Here we explored the use of an emerging technique, speckle contrast optical spectroscopy (SCOS), for blood pressure estimation. SCOS provides measurements of both blood volume changes (PPG) and blood flow index (BFi) changes during the cardiac cycle, and thus provides a richer set of parameters compared to traditional PPG. SCOS measurements were taken on the finger and wrists of 13 subjects. We investigated the correlations between features extracted from both the PPG and BFi waveforms with blood pressure. Features from the BFi waveforms were more significantly correlated with blood pressure than PPG features ( R = - 0.55, p = 1.1 × 10-4 for the top BFi feature versus R = - 0.53, p = 8.4 × 10-4 for the top PPG feature). Importantly, we also found that features combining BFi and PPG data were highly correlated with changes in blood pressure ( R = - 0.59, p = 1.7 × 10-4 ). These results suggest that the incorporation of BFi measurements should be further explored as a means to improve blood pressure estimation using non-invasive optical techniques.

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram

Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.

Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study.

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals. Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories. Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR). Results: The simulated mean ICPs in normal condition were in the normal range (8.87-11.35mmHg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (>20mmHg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean. Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.

Contactless SpO2 with an RGB camera: experimental proof of calibrated SpO2.

Camera-based blood oxygen saturation (SpO2) monitoring allows reliable measurements without touching the skin and is therefore very attractive when there is a risk of cross-infection, in case of fragile skin, and/or to improve the clinical workflow. Despite promising results, productization of the technology is hampered by the unavailability of adequate hardware, especially a camera, which can capture the optimal wavelengths for SpO2 measurements in the red near-infrared region. A regular color (RGB) camera is attractive because of its availability, but also poses several risks and challenges which affect the accuracy of the measurement. To mitigate the most important risks, we propose to add low-cost commercial off-the-shelf (COTS) components to the setup. We executed two studies with this setup: one at a hypoxia lab with SpO2 values in the range 70 - 100% with the purpose to determine the calibration model, and the other study on volunteers to investigate the accuracy for different spot-check scenarios. The proposed processing pipeline includes face tracking and a robust method to estimate the ratio of relative amplitudes of the photoplethysmographic waveforms. Results show that the error is smaller than 4 percent points for realistic screening scenarios where the subject is seated, either with or without head support and/or ambient light.

Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension.

Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.

Features of Spectral Estimates of the Photoplethysmographic Waveform Variability in Patients with Aortic Stenosis

Introduction — A well-known method for assessing the autonomic status of patients in clinical practice is the use of spectral estimates of finger's photoplethysmographic waveform variability (PPGV). However, these estimates have not been studied in patients with aortic stenosis. Therefore, this study aimed to identify in the PPGV spectrum the markers of autonomic dysfunction specific for patients with aortic stenosis. Methods — The study included 34 patients with aortic stenosis at the age of 54 (43, 67) years and 30 healthy subjects at the age of 34 (31, 36) years (data presented as median with lower and upper quartiles). The following spectral estimates of PPGV were evaluated: LF% (low-frequency band, 0.04–0.15 Hz, in the percentage of total spectral power, 0–0.4 Hz), HF% (high-frequency band, 0.15–0.4 Hz, in the percentage of total spectral power, 0–0.4 Hz), and LF/HF ratio. Results — The study revealed the statistically significant (p&lt;0.05) differences in HF% and LF% values in patients with aortic valve disease compared with healthy people. LF% was increased by 2-4 times in group of patients with aortic stenosis, while HF% was increased by 1.2-7 times. The difference between the groups at LF/HF ratio was statistically insignificant. Conclusion — Patients with aortic stenosis are characterized by higher LF% and HF% values, relative to healthy people of comparable age.

Novel Method to Assess Endothelial Function via Monitoring of Perfusion Response to Local Heating by Imaging Photoplethysmography.

Endothelial dysfunction is one of the most important markers of the risk of cardiovascular complications. This study is aimed to demonstrate the feasibility of imaging photoplethysmography to assess microcirculation response to local heating in order to develop a novel technology for assessing endothelial function. As a measure of vasodilation, we used the relative dynamics of the pulsatile component of the photoplethysmographic waveform, which was assessed in a large area of the outer surface of the middle third of the subject’s forearm. The perfusion response was evaluated in six healthy volunteers during a test with local skin heating up to 40–42 °C and subsequent relaxation. The proposed method is featured by accurate control of the parameters affecting the microcirculation during the prolonged study. It was found that in response to local hyperthermia, a multiple increase in the pulsation component, which has a biphasic character, was observed. The amplitude of the first phase of the perfusion reaction depends on both the initial skin temperature and the difference between the basal and heating temperatures. The proposed method allows the assessment of a reproducible perfusion increase in response to hyperthermia developed due to humoral factors associated with the endothelium, thus allowing detection of its dysfunction.

Amplitude and phase measurements from harmonic analysis may lead to new physiologic insights: lower body negative pressure photoplethysmographic waveforms as an example.

The photoplethysmographic (PPG) waveform contains hemodynamic information in its oscillations. We provide a new method for quantitative study of the waveform morphology and its relationship to the hemodynamics. A data adaptive modeling of the waveform shape is used to describe the PPG waveforms recorded from ear and finger. Several indices, based on the phase and amplitude information of different harmonics, are proposed to describe the PPG morphology. The proposed approach is illustrated by analyzing PPG waveforms recorded during a lower body negative pressure (LBNP) experiment. Different phase and amplitude dynamics are observed during the LBNP experiment. Specifically, we observe that the phase difference between the high order harmonics and fundamental components change more significantly when the PPG signal is recorded from the ear than the finger at the beginning of the study. In contrast, the finger PPG amplitude changes more when compared to the ear PPG during the recovery period. A more complete harmonic analysis of the PPG appears to provide new hemodynamic information when used during a LBNP experiment. We encourage other investigators who possess modulated clinical waveform data (e.g. PPG, arterial pressure, respiratory, and autonomic) to re-examine their data, using phase information and higher harmonics as a potential source of new insights into underlying physiologic mechanisms.

Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements-A Pilot Study.

(1) Background: New cuffless technologies attempting blood-pressure measurements (BPM) offer possibilities to improve hypertension awareness and control. The aim of this study was to compare a smartphone application (app)-based algorithm with office BPM (OBPM). (2) Methods: We included consecutive patients with an indication for ambulatory BPM. The smartphone app (RIVA digital) acquired the pulse wave in the fingers’ arterial bed using the phone’s camera and estimated BP based on photoplethysmographic (PPG) waveforms. Measurements were alternatingly taken with an oscillometric cuff-based device and smartphone BPM (AppBP) on two consecutive days. AppBP were calibrated to the first OBPM. Each AppBP was compared to its CuffBP (mean of the previous/following OBPM). (3) Results: 50 participants were included, resulting in 50 AppBP values on Day 1 and 33 on Day 2 after exclusion of 225 AppBP due to insufficient quality. The mean ± SD of the differences between AppBP and CuffBP was 0.7 ± 9.4/1.0 ± 4.5 mmHg (p-value 0.739/0.201) on Day 1 and 2.6 ± 8.2/1.3 ± 4.1 mmHg (p-value 0.106/0.091) on Day 2 for systolic/diastolic values, respectively. There were no significant differences between the deviations on Day 1 and Day 2 (p-value 0.297/0.533 for systolic/diastolic values). Overall, there were 10 (12%) systolic measurement pairs differing by >15 mmHg. (4) Conclusions: In this pilot evaluation, the RIVA Digital app shows promising results when compared to oscillometric cuff-based measurements, especially regarding diastolic values. Its differences between AppBP–CuffBP have a good stability one day after calibration. Before clinical use, signal acquisition needs improvement and the algorithm needs to undergo formal validation against a gold-standard BPM method.

Comparative study of photoplethysmographic waveforms with application of antihypertensive medication in hypertensive patients.

BackgroundMultiple studies have been published using a pulse oximeter's photoplethysmographic (PPG) capability to detect tissue perfusion. However, the origin of the PPG signal is still debatable.AimA comparative study was performed of PPG waveforms in hypertensive patients before and after treatment with antihypertensive medication. The aim of this study was to observe the changes of PPG waveforms before and after lowering blood pressure in hypertensive patients and then to detect the relationship between blood pressure and PPG waveforms.MethodsThe PPG waveforms of 60 patients with hypertension were collected. After administration of the antihypertensive medication nitroglycerin, PPG waveforms were collected again. The changes of the T3 (time3): This phase occurred between Marker 3 and Marker 4 (this phase occurs mid‐diastolic) angle, before and after the antihypertensive medication treatment, were compared. The statistical analyses of two related groups were performed using the Paired t‐test.ResultsThe blood perfusion waveforms of hypertensive patients before and after antihypertensive medication administration were differently indicated with the tilt angle T3. The slope angle of the T3 phase waveform increased significantly when the blood pressure dropped to normal (−41.9 ± 16.2° vs. −25.6 ± 21.9°, p < .0001), and the tilt angle of some patients was similar to that of adults with normal blood pressure.ConclusionIn patients with hypertension, the tilt angle of the PPG waveform in the T3 phase increased significantly after administration of the antihypertensive medication nitroglycerin. It is worth to conduct deeper research about the relationship between hypertension and the blood perfusion of microcirculation in the diastolic period.

Photoplethysmographic waveform detection for determining hatching egg activity via deep neural network

Photoplethysmographic waveform detection for determining hatching egg activity via deep neural network