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This article discusses a study on the prevalence of isolated inter-scalene compression in patients with suspected thoracic outlet syndrome (TOS). The study used photoplethysmography to analyze arterial and venous results in 116 subjects. The results showed that a significant proportion of limbs had isolated arterial inflow or venous outflow impairment. The authors suggest that this method of analysis can help differentiate between arterial and venous compression and potentially aid in the evaluation and treatment of TOS. Additionally, the document includes a study on TOS using photoplethysmography to assess hemodynamic responses, which found that symptoms were present in more than half of the limbs with apparently normal responses. The document also provides a list of references related to TOS, covering various aspects of the condition. [Extracted from the article]

The given text is a compilation of various research papers and studies related to photoplethysmography (PPG), a non-invasive method for measuring physiological parameters such as heart rate, blood pressure, and oxygen saturation. The papers cover a wide range of topics, including the use of PPG in remote management of COVID-19, sleep monitoring, mental health assessment, and detection of cardiovascular diseases. The studies explore different algorithms, techniques, and applications of PPG, highlighting its potential for wearable health monitoring and diagnostic purposes. The document provides a valuable resource for researchers and practitioners interested in PPG technology and its diverse applications. [Extracted from the article]

Objective. Most current algorithms for detecting atrial fibrillation (AF) rely on heart rate variability (HRV), and only a few studies analyse the variability of photopletysmography (PPG) waveform. This study aimed to compare morphological features of the PPG curve in patients with AF to those presenting a normal sinus rhythm (NSR) and evaluate their usefulness in AF detection. Approach. 10 min PPG signals were obtained from patients with persistent/paroxysmal AF and NSR. Nine morphological parameters (1/Δ T), Pulse Width [PW], augmentation index [AI], b/a, e/a, [b-e]/a, crest time [CT], inflection point area [IPA], Area and five HRV parameters (heart rate [HR], Shannon entropy [ShE], root mean square of the successive differences [RMSSD], number of pairs of consecutive systolic peaks [ R – R ] that differ by more than 50 ms [NN50], standard deviation of the R – R intervals [SDNN]) were calculated. Main results. Eighty subjects, including 33 with AF and 47 with NSR were recruited. In univariate analysis five morphological features (1/Δ T, p < 0.001; b/a, p < 0.001; [b-e]/a, p < 0.001; CT, p = 0.011 and Area, p < 0.001) and all HRV parameters (p = 0.01 for HR and p < 0.001 for others) were significantly different between the study groups. In the stepwise multivariate model (Area under the curve [AUC] = 0.988 [0.974–1.000]), three morphological parameters (PW, p < 0.001; e/a, p = 0.011; (b-e)/a, p < 0.001) and three of HRV parameters (ShE, p = 0.01; NN50, p < 0.001, HR, p = 0.01) were significant. Significance. There are significant differences between AF and NSR, PPG waveform, which are useful in AF detection algorithm. Moreover adding those features to HRV-based algorithms may improve their specificity and sensitivity. [ABSTRACT FROM AUTHOR]

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea paused or reduced breathing, respectively each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical comorbidity, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channel SpO 2 signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for automated classification of SA versus no SA using SpO 2 signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL for SpO 2 signal-based diagnosis of SA. A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility of SpO 2 signals in wearable devices for home-based SA detection. [ABSTRACT FROM AUTHOR]

Objective. Daily blood pressure (BP) monitoring is essential since BP levels can reflect the functions of heart pumping and vasoconstriction. Although various neural network-based BP estimate approaches have been proposed, they have certain practical shortcomings, such as low estimation accuracy and poor model generalization. Based on the strategy of pre-training and partial fine-tuning, this work proposes a non-invasive method for BP estimation using the photoplethysmography (PPG) signal. Approach. To learn the PPG-BP relationship, the deep convolutional bidirectional recurrent neural network (DC-Bi-RNN) was pre-trained with data from the public medical information mark for intensive care (MIMIC III) database. A tiny quantity of data from the target subject was used to fine-tune the specific layers of the pre-trained model to learn more individual-specific information to achieve highly accurate BP estimation. Main results. The mean absolute error and the Pearson correlation coefficient (r) of the proposed algorithm are 3.21 mmHg and 0.919 for systolic BP, and 1.80 mmHg and 0.898 for diastolic BP (DBP). The experimental results show that our method outperforms other methods and meets the requirements of the Association for the Advancement of Medical Instrumentation standard, and received an A grade according to the British Hypertension Society standard. Significance. The proposed method applies the strategy of pre-training and partial fine-tuning to BP estimation and verifies its effectiveness in improving the accuracy of non-invasive BP estimation. [ABSTRACT FROM AUTHOR]

Objective. The oscillometric blood pressure (BP) measurement technique estimates BPs from analyzing the oscillometric cuff pressure waveform (oscillogram) envelope. The oscillogram envelope maximum is associated with physiological changes and influences BP measurement accuracy. We aim to quantitatively investigate the effect of BP and aging on the changes of oscillogram envelope maximum. Approach. Four hundred and sixty-two subjects (214 female, 248 male) were recruited. The cuff pressure was digitally recorded during linear cuff deflation to derive oscillogram envelopes and their maximums. Moderation analysis was performed to investigate whether the relationship between BP and envelope maximum was moderated by age. Subjects were divided into five age categories and three BP groups. The envelope maximums were compared between different BP and age categories to qualify their changes with increased BP and aging. Main results. Age has a significant moderating effect on the relationship between BP and envelope maximum (P < 0.05). The oscillogram envelope maximums increased significantly with increased BPs (P < 0.05 between each BP groups) and aging (P < 0.05 for > 60 years old groups in comparison with younger groups). Significance. This study experientially and theoretically concluded the BPs and aging are two important factors that influence the maximum value of the oscillogram envelope. [ABSTRACT FROM AUTHOR]

Objective. Noninvasive measurement of oxygen saturation (SpO 2) using transmissive photoplethysmography (tPPG) is clinically accepted and widely employed. However, reflective photoplethysmography (rPPG)â€"currently present in smartwatchesâ€"has not become equally accepted, partially because the pathlengths of the red and infrared PPGs are patient-dependent. Thus, even the most popular ‘Ratio of Modulation’ (R) method requires patient-dependent calibration to reduce the errors in the measurement of SpO 2 using rPPGs. Approach. In this paper, a correction factor or ‘pathlength ratio’ β is introduced in an existing calibration-free algorithm that compensates the patient-dependent pathlength variations, and improved accuracy is obtained in the measurement of SpO 2 using rPPGs. The proposed pathlength ratio β is derived through the analytical model of a rPPG signal. Using the new expression and data obtained from a human hypoxia study wherein arterial oxygen saturation values acquired through Blood Gas Analysis were employed as a reference, β is determined. Main results. The results of the analysis show that a specific combination of the β and the measurements on the pulsating part of the natural logarithm of the red and infrared PPG signals yields a reduced root-mean-square error (RMSE). It is shown that the average RMSE in measuring SpO 2 values reduces to 1 %. Significance. The human hypoxia study data used for this work, obtained in a previous study, covers SpO 2 values in the range from 70 % to 100 %, and thus shows that the pathlength ratio β proposed here works well in the range of clinical interest. This work demonstrates that the calibration-free method applicable for transmission type PPGs can be extended to determine SpO 2 using reflective PPGs with the incorporation of the correction factor β. Our algorithm significantly reduces the number of parameters needed for the estimation, while keeping the RMSE below the clinically accepted 2 %. [ABSTRACT FROM AUTHOR]

Objective. This study aimed to describe a modified photoplethysmography (PPG) signal processing and analysis procedure to obtain a more reliable arterial stiffness index (SI). Approach. Three parameters were used to assess the PPG signal quality without prominent diastolic waves, which are similar to a sinusoidal waveform shape. The first parameter, sinusoidal ratio (S-value), was based on frequency-domain analysis: a higher S-value indicated the presence of PPG pulse wave with unapparent diastolic peak. The second parameter was the time difference between systolic peak-to-diastolic peak and the systolic peak-to-dicrotic notch. The third parameter was the percentage of sin-like waveform in the PPG signals. The applicability of these parameters was demonstrated in 40 participants, including 11 with apparent diastolic peaks in the PPG signals and 29 with unapparent diastolic peaks. Main results. An S-value of >3.5 indicated apparent diastolic peaks in the PPG signals. In addition, a systolic peak-to-diastolic peak time difference >80% and a sin-like waveform >55% may be associated with severity of vascular aging. Significance. These parameters successfully detected low-quality PPG signals with unapparent diastolic waveform before SI calculation, thereby ensuring the accuracy of subsequent evaluation of cardiovascular-related disease and clinical risk stratification. [ABSTRACT FROM AUTHOR]

Objective . The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications. Approach . Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints. Significance . The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective. Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF). Approach. Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson's correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF. Main Results. Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min -1 m -2 , with ranging from 2.6 to 4.77 l min -1 m -2 . The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57, p < 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 ( p = 0.35) and 0.03 ( p = 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 ( p < 0.01). Significance. This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated., (Creative Commons Attribution license.)

Objective. In this study we explored the utility of post-prandial vascular stiffness as a surrogate measure for estimating insulin resistance, which is a pre-diabetic condition. Approach. A cohort of 51 healthy young adults with varying body mass index (BMI) values was studied using fasting plasma values of insulin and glucose, fasting and post-meal finger photoplethysmography (PPG) and electrocardiogram (ECG). Insulin resistance was estimated by homeostatic model assessment for insulin resistance 2 (HOMA-IR2) using fasting plasma insulin and glucose. Vascular stiffness was estimated by reciprocal of pulse arrival time (rPAT) from ECG and finger PPG at five time points from fasting to 2 h post-oral glucose ingestion. We examined if insulin resistance correlates with meal-induced vascular stiffness changes, supporting the feasibility of using finger PPG to estimate insulin resistance. Main results. HOMA-IR2 was positively correlated with an early rise (0 to 30 min post-meal) and delayed fall (30 to 120 min post-meal) of rPAT. Correlation persisted even after the effect of BMI has been partialled out in subgroup analysis. We conclude that finger PPG-based pulse waveform and single-lead ECG has the potential to be used as a non-invasive method for the assessment of insulin resistance. Significance. As both signals, namely ECG and PPG, can be easily acquired using wearable and other low-cost sensing systems, the present study can serve as a pointer to develop accessible strategies for monitoring and longitudinal tracking of insulin resistance in health and pathophysiological states. [ABSTRACT FROM AUTHOR]

Objective. Autonomic nervous system function and thereby bodily stress and recovery reactions may be assessed by wearable devices measuring heart rate (HR) and its variability (HRV). So far, the validity of HRV-based stress assessments has been mainly studied in healthy populations. In this study, we determined how psychosocial stress affects physiological and psychological stress responses in both young (18â€"30 years) and middle-aged (45â€"64 years) healthy individuals as well as in patients with arterial hypertension and/or either prior evidence of prediabetes or type 2 diabetes. We also studied how an HRV-based stress index (Relax-Stress Intensity, RSI) relates to perceived stress (PS) and cortisol (CRT) responses during psychosocial stress. Approach. A total of 197 participants were divided into three groups: (1) healthy young (HY, N  = 63), (2) healthy middle-aged (HM, N  = 61) and (3) patients with cardiometabolic risk factors (Pts, N  = 73, 32â€"65 years). The participants underwent a group version of Trier Social Stress Test (TSST-G). HR, HRV (quantified as root mean square of successive differences of Râ€"R intervals, RMSSD), RSI, PS, and salivary CRT were measured regularly during TSST-G and a subsequent recovery period. Main results. All groups showed significant stress reactions during TSST-G as indicated by significant responses of HR, RMSSD, RSI, PS, and salivary CRT. Between-group differences were also observed in all measures. Correlation and regression analyses implied RSI being the strongest predictor of CRT response, while HR was more closely associated with PS. Significance. The HRV-based stress index mirrors responses of CRT, which is an independent marker for physiological stress, around TSST-G. Thus, the HRV-based stress index may be used to quantify physiological responses to psychosocial stress across various health and age groups. [ABSTRACT FROM AUTHOR]

Photoplethysmography is now widely utilised by clinical devices such as pulse oximeters, and wearable devices such as smartwatches. It holds great promise for health monitoring in daily life. This editorial considers whether it would be possible and beneficial to establish best practices for photoplethysmography signal acquisition and processing. It reports progress made towards this, balanced with the challenges of working with a diverse range of photoplethysmography device designs and intended applications, each of which could benefit from different approaches to signal acquisition and processing. It concludes that there are several potential benefits to establishing best practices. However, it is not yet clear whether it is possible to establish best practices which hold across the range of photoplethysmography device designs and applications. [ABSTRACT FROM AUTHOR]

Objective. To observe changes in baseline position and pulsatile light absorbance (photoplethysmograph, PPG) in the finger-tip, by raising the hand above the horizontal plane in recumbent subjects. We applied current knowledge of the circulation to the finger-tip, particularly arteriovenous anastomoses (AVAs), and the physiology of the venous circulation. Approach.We studied healthy young volunteers in a quiet thermoneutral environment. A finger plethysmograph on the non-dominant hand recorded transmission of red and infra-red light, with observations expressed as absorbance to allow comparisons within and between subjects. Breathing movements were recorded unobtrusively to assess any effect on absorbance and the pulse amplitude of the signals. All body movements were passive: the study arm was elevated in a trough to about 40° above the horizontal plane. The following conditions were studied, each for 15 min, using the last 10 min for analysis: recumbent, study arm elevated, study arm horizontal, and both legs elevated by 40°. Main results. We found a substantial time-related effect, and considerable variation between subjects. Arm elevation reduced red light absorbance and increased the range of amplitudes of the PPG waveform: only in subjects with large absorbances, did waveform amplitude increase. Spontaneous, thermoregulatory decreases in absorbance were large and associated with decreases in waveform amplitude. Significance. Finger-tip vessels distend with blood and light absorbance increases when AVAs open. The vessels pulsate more strongly when the hand is raised: venous collapse allows the vessels to become more compliant. The postcapillary circulation is likely to be an important source of pulsation. [ABSTRACT FROM AUTHOR]

Objective. Wearable devices equipped with plethysmography (PPG) sensors provided a low-cost, long-term solution to early diagnosis and continuous screening of heart conditions. However PPG signals collected from such devices often suffer from corruption caused by artifacts. The objective of this study is to develop an effective supervised algorithm to locate the regions of artifacts within PPG signals. Approach. We treat artifact detection as a 1D segmentation problem. We solve it via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA testing set, WESAD dataset and TROIKA dataset. Main results. We evaluated with the DICE score, a well-established metric for segmentation accuracy evaluation in the field of computer vision. The proposed method outperforms baseline methods on all three datasets by a large margin (≈7 percentage points above the next best method). On the PPG DaLiA testing set, WESAD dataset and TROIKA dataset, the proposed method achieved 0.8734 ± 0.0018, 0.9114 ± 0.0033 and 0.8050 ± 0.0116 respectively. The next best method only achieved 0.8068 ± 0.0014, 0.8446 ± 0.0013 and 0.7247 ± 0.0050. Significance. The proposed method is able to pinpoint exact locations of artifacts with high precision; in the past, we had only a binary classification of whether a PPG signal has good or poor quality. This more nuanced information will be critical to further inform the design of algorithms to detect cardiac arrhythmia. [ABSTRACT FROM AUTHOR]

Objective. Diagnosis of incipient acute hypovolemia is challenging as vital signs are typically normal and patients remain asymptomatic at early stages. The early identification of this entity would affect patients' outcome if physicians were able to treat it precociously. Thus, the development of a noninvasive, continuous bedside monitoring tool to detect occult hypovolemia before patients become hemodynamically unstable is clinically relevant. We hypothesize that pulse oximeter's alternant (AC) and continuous (DC) components of the infrared light are sensitive to acute and small changes in patient's volemia. We aimed to test this hypothesis in a cohort of healthy blood donors as a model of slight hypovolemia. Approach. We planned to prospectively study blood donor volunteers removing 450 ml of blood in supine position. Noninvasive arterial blood pressure, heart rate, and finger pulse oximetry were recorded. Data was analyzed before donation, after donation and during blood auto-transfusion generated by the passive leg-rising (PLR) maneuver. Main results. Sixty-six volunteers (44% women) accomplished the protocol successfully. No clinical symptoms of hypovolemia, arterial hypotension (systolic pressure < 90 mmHg), brady-tachycardia (heart rate <60 and >100 beats-per-minute) or hypoxemia (SpO 2 < 90%) were observed during donation. The AC signal before donation (median 0.21 and interquartile range 0.17 a.u.) increased after donation [0.26(0.19) a.u; p < 0.001]. The DC signal before donation [94.05(3.63) a.u] increased after blood extraction [94.65(3.49) a.u; p < 0.001]. When the legs' blood was auto-transfused during the PLR, the AC [0.21(0.13) a.u.; p = 0.54] and the DC [94.25(3.94) a.u.; p = 0.19] returned to pre-donation levels. Significance. The AC and DC components of finger pulse oximetry changed during blood donation in asymptomatic volunteers. The continuous monitoring of these signals could be helpful in detecting occult acute hypovolemia. New pulse oximeters should be developed combining the AC/DC signals with a functional hemodynamic monitoring of fluid responsiveness to define which patient needs fluid administration., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective.  Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. Approach.  This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies. Significance.  Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis., (Creative Commons Attribution license.)

Objective. The waveform of a photoplethysmography (PPG) signal depends on the measurement site and individual physiological conditions. Filtering can distort the morphology of the original PPG signal waveform and change the timing of pulse feature points on PPG signals. We aim to quantitatively investigate the effect of PPG signal morphology (related to measurement site) and type of pulse feature on the filtering-induced time shift (TS). Approach. 60 s PPG signals were measured from six body sites (finger, wrist under (volar), wrist upper (dorsal), earlobe, and forehead) of 36 healthy adults. Using infinite impulse response digital filters which are common in PPG signal processing, PPG signals were prefiltered (band-pass, pass and stop bands: >0.5 Hz and <0.2 Hz for high-pass filter, <20 Hz and >30 Hz for low-pass filter) and then filtered (low-pass, pass and stop bands: <3 Hz and >5 Hz). Four pulse feature points were defined and extracted (peak, valley, maximal first derivative, and maximal second derivative). For each subject, overall TS and intra-subject TS variability in feature points were calculated as the mean and standard deviation of TS between prefiltered and filtered PPG signals in 50 cardiac cycles. Statistical testing was performed to investigate the effect of measurement site and type of pulse feature on overall TS and intra-subject TS variability. Main results. Measurement site, type of pulse feature, and their interaction had significant impacts on the overall TS and intra-subject TS variability (p < 0.001 for all). Valley and maximal second derivative showed higher overall TS than peak and maximal first derivative. Finger had higher overall TS and lower intra-subject TS variability than other measurement sites. Significance. Measurement site and type of pulse feature can significantly influence the timing of feature points on filtered PPG signals. Filtering parameters should be quoted to support the reproducibility of PPG-related studies. [ABSTRACT FROM AUTHOR]

Objective. Long-term abnormal blood pressure (BP) can lead to various cardiovascular diseases; therefore, it is significant to assess BP status as a preventative measure. In this study, a feature-extraction-based approach is proposed and performed on an open clinical trial dataset. Approach. Firstly, a complete ensemble of empirical mode decomposition with an adaptive noise algorithm and wavelet threshold analysis is applied to eliminate the noise interference from an original photoplethysmography (PPG) signal compared to other signal filters. Considering the strong connection between hypertension and diabetes, an analysis of variance test with a 95% confidence interval is firstly carried out to select these leading extracted morphological features, which are uniquely related to hypertension, from the PPG signal and its derivatives. Subsequently a variety of classification models are evaluated at different BP levels and their performances are compared. Main results and Significance. The test results demonstrate that the support vector machine classification model achieves a greater performance compared to other explored models in this paper, with accuracy of 78%, 87% and 88% for cases including normal versus prehypertension subjects, normotension versus hypertension subjects and non-hypertension versus hypertension subjects, respectively, which further illustrates the great potential of the proposed method in hypertension assessment. [ABSTRACT FROM AUTHOR]

Objective. We present the design and validation of a non-invasive smart-phone based screening tool for atherosclerosis and coronary arterial disease (CAD), which is the leading cause of mortality worldwide. Approach. We designed a three-channel photoplethysmography (PPG) device that connects to a smart phone application for measuring pulse transit time (PTT) and pulse wave velocity (PWV) using PPG probes that are simultaneously clipped onto to the ear, index finger, and big toe, respectively. Validation was performed through a clinical study with 100 participants (age 20 to 77) at a research hospital in Nagpur, India. Study subjects were stratified by age and divided into three groups corresponding to the disease severity: CAD, hypertensive ('Pre-CAD'), and Healthy. Main results. PWV measurements derived from the Ear-Toe probe measurements yielded the best performance, with median PWV values increasing monotonically as a function of disease severity and age, as follows: 14.2 m s−1 for the older-patient CAD group, 12.2 m s−1 for the younger-patient CAD group, 11.6 m s−1 for the older-patient Pre-CAD group, 10.2 m s−1 for the younger-patient Pre-CAD group, 9.7 m s−1 for the older healthy controls, and 8.4 m s−1 for the younger healthy controls. Using just two simple features, the PTT and patient height, we demonstrate a machine learning prediction model for CAD with a median accuracy of 0.83 (AUC). Significance. This work demonstrates the ability to predict atherosclerosis and CAD using a single simple physiological measurement with a multi-site PPG tool that is electrically powered by a mobile phone and does not require any electrocardiogram reference. Furthermore, this method only requires a single anthropometric measurement, which is the patient's height. [ABSTRACT FROM AUTHOR]

Objective. A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG ('DLPPG') classification method to detect peripheral arterial disease (PAD) using toe PPG signals. Approach. PPG spectrogram images derived from our previously published multi-site PPG datasets (214 participants; 31.3% legs with PAD by ankle brachial pressure index (ABPI)) were input into a pretrained 8-layer (five convolutional layers + three fully connected layers) AlexNet as tailored to the 2-class problem with transfer learning to fine tune the convolutional neural network (CNN). k-fold random cross validation (CV) was performed (for k = 5 and k = 10), with each evaluated over k training/validation runs. Overall test sensitivity, specificity, accuracy, and Cohen's Kappa statistic with 95% confidence interval ranges were calculated and compared, as well as sensitivities in detecting mild-moderate (0.5 ≤ ABPI < 0.9) and major (ABPI < 0.5) levels of PAD. Main results. CV with either k = 5 or 10 folds gave similar diagnostic performances. The overall test sensitivity was 86.6%, specificity 90.2% and accuracy 88.9% (Kappa: 0.76 [0.70–0.82]) (at k = 5). The sensitivity to mild-moderate disease was 83.0% (75.5%–88.9%) and to major disease was 100.0% (90.5%–100.0%). Significance. Substantial agreements have been demonstrated between the DL-based PPG classification technique and the ABPI PAD diagnostic reference. This novel automatic approach, requiring minimal pre-processing of the pulse waveforms before PPG trace classification, could offer significant benefits for the diagnosis of PAD in a variety of clinical settings where low-cost, portable and easy-to-use diagnostics are desirable. [ABSTRACT FROM AUTHOR]

Objective. To develop a sleep staging method from wrist-worn accelerometry and the photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram (ECG) database. Approach. In previous work, we developed a deep convolutional neural network for sleep staging from ECG using the cross-spectrogram of ECG-derived respiration and instantaneous beat intervals, heart rate variability metrics, spectral characteristics, and signal quality measures derived from 5793 subjects in Sleep Heart Health Study (SHHS). We updated the weights of this model by transfer learning using PPG data derived from the Empatica E4 wristwatch worn by 105 subjects in the 'Emory Twin Study Follow-up' (ETSF) database, for whom overnight polysomnographic (PSG) scoring was available. The relative performance of PPG, and actigraphy (Act), plus combinations of these two signals, with and without transfer learning was assessed. Main results. The performance of our model with transfer learning showed higher accuracy (1–9 percentage points) and Cohen's Kappa (0.01–0.13) than those without transfer learning for every classification category. Statistically significant, though relatively small, incremental differences in accuracy occurred for every classification category as tested with the McNemar test. The out-of-sample classification performance using features from PPG and actigraphy for four-class classification was Accuracy (Acc) = 68.62% and Kappa = 0.44. For two-class classification, the performance was Acc = 81.49% and Kappa = 0.58. Significance. We proposed a combined PPG and actigraphy-based sleep stage classification approach using transfer learning from a large ECG sleep database. Results demonstrate that the transfer learning approach improves estimates of sleep state. The use of automated beat detectors and quality metrics means human over-reading is not required, and the approach can be scaled for large cross-sectional or longitudinal studies using wrist-worn devices for sleep staging. [ABSTRACT FROM AUTHOR]

Objective. For the first time in the literature, this paper investigates some crucial aspects of blood pressure (BP) monitoring using photoplethysmogram (PPG) and electrocardiogram (ECG). In general, the proposed approaches utilize two types of features: parameters extracted from physiological models or machine-learned features. To provide an overview of the different feature extraction methods, we assess the performance of these features and their combinations. We also explore the importance of the ECG waveform. Although ECG contains critical information, most models merely use it as a time reference. To take this one step further, we investigate the effect of its waveform on the performance. Approach. We extracted 27 commonly used physiological parameters in the literature. In addition, convolutional neural networks (CNNs) were deployed to define deep-learned representations. We applied the CNNs to extract two different feature sets from the PPG segments alone and alongside corresponding ECG segments. Then, the extracted feature vectors and their combinations were fed into various regression models to evaluate our hypotheses. Main results. We performed our evaluations using data collected from 200 subjects. The results were analyzed by the mean difference t-test and graphical methods. Our results confirm that the ECG waveform contains important information and helps us to improve accuracy. The comparison of the physiological parameters and machine-learned features also reveals the superiority of machine-learned representations. Moreover, our results highlight that the combination of these feature sets does not provide any additional information. Significance. We conclude that CNN feature extractors provide us with concise and precise representations of ECG and PPG for BP monitoring. [ABSTRACT FROM AUTHOR]

Objective. Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz. Approach. The method applies template matching to leverage the random nature of sampling time and expected change in the PAT. Main results. The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively. Significance. This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings., (Creative Commons Attribution license.)

Objective. Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date. Approach.  The PPG-beats open-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors named QPPG , MSPTD and ABD among others. We applied the PPG-beats framework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings. Main Results.  In a clinical setting, the QPPG beat detector performed best on atrial fibrillation (with a median F 1 score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behind ABD detector (76.1%). In an ambulatory setting, the MSPTD beat detector performed best on normal sinus rhythm (94.6%), and the QPPG detector on atrial fibrillation (91.6%) and bigeminy (80.0%). Significance.  Overall, the PPG beat detectors QPPG , MSPTD and ABD consistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia., (Creative Commons Attribution license.)

Objective: Deep inferior epigastric perforator (DIEP) free flaps are widely used as a reconstruction option following mastectomy in breast cancer. During such cases partial tissue necrosis can occur due to the insufficient blood supply to the transplanted tissue site. Therefore, monitoring of flap perfusion and early detection of flap failure is a prerequisite to flap survival. There is a need to develop a non-invasive, easy to use, reproducible and inexpensive monitoring device to assess flap perfusion postoperatively. Approach: A three-wavelength reflective optical sensor and processing system based on the principle of photoplethysmography (PPG) has been developed to investigate blood volumetric changes and estimate free flap blood oxygen saturation continuously and non-invasively in DIEP free flaps in the postoperative period. The system was evaluated in 15 patients undergoing breast reconstructive surgery using DIEP free flap. Main results and Significance: Good quality red, infrared and green PPG signals were obtained in the postoperative period. Initial estimation of blood oxygen saturation values estimated from the free flap PPGs seem to be in broad agreement with the commercial finger pulse oximeter used in this study. This pilot study has demonstrated that PPG has the potential to be used as a monitoring technique in assessing free flap viability. [ABSTRACT FROM AUTHOR]

Objective: Photoplethysmography imaging (PPGI) has gained immense attention over the last few years but only a few works have addressed morphological analysis so far. Pulse wave decomposition (PWD), i.e. the decomposition of a pulse wave by a varying number of kernels, allows for such analyses. This work investigates the applicability of PWD algorithms in the context of PPGI. Approach: We used simulated and experimental data to compare various PWD algorithms from the literature regarding their robustness against noise and motion artifacts while preserving morphological information as well as regarding their ability to reveal physiological changes by PPGI. Main results: Our experiments prove that algorithms that combine Gamma and Gaussian distributions outperform other choices. Further, algorithms with two kernels exhibit the highest robustness against noise and motion artifacts (improvement in of 14.09 %) while preserving the morphology similarly to algorithms using more kernels. Lastly, we showed that PWD can reveal physiological changes upon distal stimuli by PPGI. Significance: This work proves the feasibility of pulse decomposition analysis in PPGI, particularly for algorithms with a low number of kernels, and opens up novel applications for PPGI. Not only for PPGI but for future research on PWD in general, our findings have importance as they elucidate differences between PWD algorithms and emphasize the importance of using initial values. To support such future research, we have released the algorithms and simulated data to the public. [ABSTRACT FROM AUTHOR]

Objective: Based on different physiological mechanisms, the respiratory modulations of photoplethysmography (PPG) signals differ in strength and resultant accuracy of respiratory frequency (RF) estimations. We aimed to investigate the strength of different respiratory modulations and the accuracy of resultant RF estimations in different body sites and two breathing patterns. Approach: PPG and reference respiratory signals were simultaneously measured over 60 s from 36 healthy subjects in six sites (arm, earlobe, finger, forehead, wrist-under (volar side), wrist-upper (dorsal side)). Respiratory signals were extracted from PPG recordings using four demodulation approaches: amplitude modulation (AM), baseline wandering (BW), frequency modulation (FM) and filtering. RFs were calculated from the PPG-derived and reference respiratory signals. To investigate the strength of respiratory modulations, the energy proportion in the range that covers 75% of the total energy in the reference respiratory signal, with RF in the middle, was calculated and compared between different modulations. Analysis of variance and the Scheirer–Ray–Hare test were performed with post hoc analysis. Mainresults: In normal breathing, FM was the only modulation whose RF was not significantly different from the reference RF (p > 0.05). Compared with other modulations, FM was significantly higher in energy proportion (p < 0.05) and lower in RF estimation error (p < 0.05). As to energy proportion, measurements from the finger and the forehead were not significantly different (p > 0.05), but both were significantly different from the other four sites (p < 0.05). In deep breathing, the RFs derived by BW, filtering and FM were not significantly different from the reference RF (p > 0.05). The RF estimation error of FM was significantly less than that of AM or BW (p < 0.05). The energy proportion of FM was significantly higher than that of other modulations (p < 0.05). Significance: Of all the respiratory modulations, FM has the highest strength and is appropriate for accurate RF estimation from PPG signals recorded at different sites and for different breathing patterns. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is a photometric technique used for the measurement of volumetric changes in the blood. The recent interest in new applications of PPG has invigorated more fundamental research regarding the origin of the PPG waveform, which since its discovery in 1937, remains inconclusive. A handful of studies in the recent past have explored various hypotheses for the origin of PPG. These studies relate PPG to mechanical movement, red blood cell orientation or blood volume variations. Objective: Recognising the significance and need to corroborate a theory behind PPG formation, the present work rigorously investigates the origin of PPG based on a realistic model of light–tissue interactions. Approach: A three-dimensional comprehensive Monte Carlo model of finger-PPG was developed and explored to quantify the optical entities pertinent to PPG (e.g. absorbance, reflectance, and penetration depth) as the functions of multiple wavelengths and source-detector separations. Complementary to the simulations, a pilot in vivo investigation was conducted on eight healthy volunteers. PPG signals were recorded using a custom-made multiwavelength sensor with an adjustable source-detector separation. Main results: Simulated results illustrate the distribution of photon–tissue interactions in the reflectance PPG geometry. The depth-selective analysis quantifies the contributions of the dermal and subdermal tissue layers in the PPG wave formation. A strong negative correlation (r = −0.96) is found between the ratios of the simulated absorbances and measured PPG amplitudes. Significance: This work quantified for the first time the contributions of different tissue layers and sublayers in the formation of the PPG signal. [ABSTRACT FROM AUTHOR]

Heart rate variability has been largely used for the assessment of cardiac autonomic activity, due to the direct relationship between cardiac rhythm and the activity of the sympathetic and parasympathetic nervous system. In recent years, another technique, pulse rate variability, has been used for assessing heart rate variability information from pulse wave signals, especially from photoplethysmography, a non-invasive, non-intrusive, optical technique that measures the blood volume in tissue. The relationship, however, between pulse rate variability and heart rate variability is not entirely understood, and the effects of cardiovascular changes in pulse rate variability have not been thoroughly elucidated. In this review, a comprehensive summary of the applications in which pulse rate variability has been used, with a special focus on cardiovascular health, and of the studies that have compared heart rate variability and pulse rate variability is presented. It was found that the relationship between heart rate variability and pulse rate variability is not entirely understood yet, and that pulse rate variability might be influenced not only due to technical aspects but also by physiological factors that might affect the measurements obtained from pulse-to-pulse time series extracted from pulse waves. Hence, pulse rate variability must not be considered as a valid surrogate of heart rate variability in all scenarios, and care must be taken when using pulse rate variability instead of heart rate variability. Specifically, the way pulse rate variability is affected by cardiovascular changes does not necessarily reflect the same information as heart rate variability, and might contain further valuable information. More research regarding the relationship between cardiovascular changes and pulse rate variability should be performed to evaluate if pulse rate variability might be useful for the assessment of not only cardiac autonomic activity but also for the analysis of mechanical and vascular autonomic responses to these changes. [ABSTRACT FROM AUTHOR]

Objective: Sleep quality has a significant impact on human mental and physical health. The detection of sleep–wake states is thus of paramount importance in the study of sleep. The gold standard method for sleep–wake classification is multi-sensor-based polysomnography (PSG) which is normally recorded in a clinical setting. The main drawbacks of PSG are the inconvenience to the subjects, the impact of discomfort on normal sleep cycles, and its requirement for experts' interpretation. In contrast, we aim to design an automated approach for sleep–wake classification using a wearable fingertip photoplethysmographic (PPG) signal. Approach: Time domain features are extracted from PPG and PPG-based surrogate cardiac signals for sleep–wake classification. A minimal-redundancy-maximal-relevance feature selection algorithm is employed to reduce irrelevant and redundant features. Main results: A support vector machine (SVM)-based supervised machine-learning classifier is then used to classify sleep and wake states. The model is trained using 70% of the events (6575 sleep–wake events) from the dataset, and the remaining 30% of events (2818 sleep–wake events) are used for evaluating the performance of the model. Furthermore, the proposed model demonstrates a comparable performance (accuracy 81.10%, sensitivity 81.06%, specificity 82.50%, precision 99.37%, and F score 81.74%) with respect to the existing uni-modal and multi-modal methods for sleep–wake classification. Significance: This result advocates the potential of wearable PPG-based sleep–wake classification. A wearable PPG-based system would help in continuous, non-invasive monitoring of sleep quality. [ABSTRACT FROM AUTHOR]

Objective: It is accepted that changes in the peripheral pulse waveform characteristics occur with ageing. Pulse risetime is one important feature which has clinical value. However, it is unclear how it varies across the full age spectrum from child to senior and for different peripheral measurement sites. The objectives of this study were to determine the association between age and pulse risetime characteristics over an 8-decade age range at the ears, fingers, and toes, and to consider effects arising from differences in systolic blood pressure (SBP), height and heart rate. Approach: Multi-site photoplethysmography (MPPG) pulse waveforms were recorded non-invasively from the right and left ears, fingers, and toes of 304 normal healthy human subjects (range 6–87 years; 156 male and 148 female). SBP, height, and heart rate were also measured. Multi-site PPG pulse risetimes, and their site differences, were determined. Main results: Univariate regression analysis showed positive correlations with risetime for age (ears, fingers and toes: + 0.8, + 1.9, and + 1.1 ms/year, respectively), SBP (+0.5, + 1.3, and + 0.9 ms/mmHg) and height (+0.5, + 1.2, and + 1.0 ms/cm), but with a clear inverse association with heart rate (−1.8, − 2.5, and − 1.6 ms min) (P < 0.0001). No significant differences between male and female subjects were found for pulse risetime. Significance: Normative multi-site PPG risetime characteristics have been defined in over 300 subjects and are shown to increase with age linearly up to the 8th decade. In contrast, we have shown that heart rate has a clear inverse relationship with risetime for all measurement sites. [ABSTRACT FROM AUTHOR]

Objective: Respiratory activity is an essential parameter to monitor healthy and disordered sleep, and unobtrusive measurement methods have important clinical applications in diagnostics of sleep-related breathing disorders. We propose a respiratory activity surrogate extracted from wrist-worn reflective photoplethysmography validated on a heterogeneous dataset of 389 sleep recordings. Approach: The surrogate was extracted by interpolating the amplitude of the PPG pulses after evaluation of pulse morphological quality. Subsequent multistep post-processing was applied to remove parts of the surrogate with low quality and high motion levels. In addition to standard respiration rate performance, we evaluated the similarity between surrogate respiratory activity and reference inductance plethysmography of the thorax, using Spearman's correlations and spectral coherence, and assessed the influence of PPG signal quality, motion levels, sleep stages and obstructive sleep apnea. Main results: Prior to post-processing, the surrogate already had a strong similarity with the reference (correlation = 0.54, coherence = 0.81), and reached respiration rate estimation performance in line with the literature (estimation error = 0.76± 2.11 breaths/min). Detrimental effects of low PPG quality, high motion levels and sleep-dependent physiological phenomena were significantly mitigated by the proposed post-processing steps (correlation = 0.62, coherence = 0.88, estimation error = 0.53± 1.85 breaths/min). Significance: Wrist-worn PPG can be used to extract respiratory activity, thus allowing respiration monitoring in real-world sleep medicine applications using (consumer) wearable devices. [ABSTRACT FROM AUTHOR]

Objective: Photoplethysmography imaging (PPGI) is a promising contactless camera-based method of non-invasive cardiovascular diagnostics. To achieve the best results, it is important to choose the most suitable camera for a specific application. The settings of the camera influence the quality of the detected signal. Approach: The standard (European Machine Vision Association 2016 EMVA Standard 1288—Standard for Characterization of Image Sensors and Cameras pp 1–39 (available at: https://www.emva.org/wp-content/uploads/EMVA1288-3.1a.pdf)) for evaluating the imaging performance of machine vision cameras (MVC) helps at the initial decision of the sensor, but the camera should always be tested in terms of usability for a specific application. So far, PPGI lacks a standardized measurement scenario for evaluating the performance of individual cameras. For this, we realized a controllable optoelectronic phantom with artificial silicone skin allowing reproducible tests of cameras to verify their suitability for PPGI. The entire system is housed in a light-tight box. We tested an MVC, a digital single-lens reflex camera (DSLR) camera and a webcam. Each camera varies in used technology and price. Main results: We simulated real PPGI measurement conditions simulating the ratio of pulse (AC) and non-pulse (DC) component of the photoplethysmographic signal and achieved AC/DC ratios of 0.5 % on average. An additional OLED panel ensures proper DC providing reproducible measurement conditions. We evaluated the signal morphological features, amplitude spectrum, signal-to-noise ratio (SNR) and spatially dependent changes of simulated subcutaneous perfusion. Here, the MVC proved to be the most suitable device. A DSLR is also suitable for PPGI, but a larger smoothing kernel is required to obtain a perfusion map. The webcam, as the weakest contender, proved to be very susceptible to any inhomogeneous illumination of the examined artificial skin surface. However, it is still able to detect cardiac rhythm. Significance: The result of our work is an optoelectronic phantom for reproducible testing of PPGI camera performance in terms of signal quality and measurement equipment costs. [ABSTRACT FROM AUTHOR]

Objective. Photoplethysmography (PPG) is a promising wearable technology that detects volumetric changes in microcirculation using a light source and a sensor on the skin's surface. PPG has been shown to be useful for non-invasive blood pressure (BP) measurement. Deep learning-based BP measurements are now gaining popularity. However, almost all methods focus on 1D PPG. We aimed to design an end-to-end approach for estimating BP using image encodings from a 2D perspective. Approach. In this paper, we present a BP estimation approach based on an image encoding and fusion (BP-IEF) technique. We convert the PPG into five image encodings and use them as input. The proposed BP-IEF consists of two parts: an encoder and a decoder. In addition, three kinds of well-known neural networks are taken as the fundamental architecture of the encoder. The decoder is a hybrid architecture that consists of convolutional and fully connected layers, which are used to fuse features from the encoder. Main results. The performance of the proposed BP-IEF is evaluated on the UCI database in both non-mixed and mixed manners. On the non-mixed dataset, the root mean square error and mean absolute error for systolic BP (SBP) are 13.031 mmHg and 9.187 mmHg respectively, while for diastolic BP (DBP) they are 5.049 mmHg and 3.810 mmHg. On the mixed dataset, the corresponding values for SBP are 4.623 mmHg and 3.058 mmHg, while for DBP the values are 2.350 mmHg and 1.608 mmHg. In addition, both SBP and DBP estimation on the mixed dataset achieved grade A compared to the British Hypertension Society standard. The DBP estimation on the non-mixed dataset also achieved grade A. Significance. The results indicate that the proposed approach has the potential to improve on the current mobile healthcare for cuffless BP measurement., (© 2023 Institute of Physics and Engineering in Medicine.)

Objective. Physiological sensor data (e.g. photoplethysmograph) is important for remotely monitoring patients' vital signals, but is often affected by measurement noise. Existing feature-based models for signal cleaning can be limited as they might not capture the full signal characteristics. Approach. In this work we present a deep learning framework for sensor signal cleaning based on dilated convolutions which capture the coarse- and fine-grained structure in order to classify whether a signal is noisy or clean. However, since obtaining annotated physiological data is costly and time-consuming we propose an autoencoder-based semi-supervised model which is able to learn a representation of the sensor signal characteristics, also adding an element of interpretability. Main results. Our proposed models are over 8% more accurate than existing feature-based approaches with half the false positive/negative rates. Finally, we show that with careful tuning (that can be improved further), the semi-supervised model outperforms supervised approaches suggesting that incorporating the large amounts of available unlabeled data can be advantageous for achieving high accuracy (over 90%) and minimizing the false positive/negative rates. Significance. Our approach enables us to reliably separate clean from noisy physiological sensor signal that can pave the development of reliable features and eventually support decisions regarding drug efficacy in clinical trials., (Creative Commons Attribution license.)

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology., (Creative Commons Attribution license.)

Precise beat-to-beat fiducial point detection in the photoplethysmogram signal is essential for reliable pulse rate variability (PRV) analysis, which is considered an integral part of health monitoring devices in the evolving era of mobile health. Several studies have aimed to compare PRV to the well-investigated, gold standard heart rate variability (HRV) analysis, to see if they are interchangeable. The agreement between PRV and HRV is not unequivocal, as we learn from the commented metaanalysis. Technical factors like low sampling rate of photoplethysmography (PPG) or imprecise fiducial point detection are more important in this difference than physiological factors corresponding to pulse arrival time. Standardization of the PPG acquisition and reference point detection is necessary for comparable studies and correct measurement. [ABSTRACT FROM AUTHOR]

Objective . Pulse oximetry is a non-invasive optical technique used to measure arterial oxygen saturation (SpO 2 ) 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., (Creative Commons Attribution license.)

Objective . The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human cardiovascular status information than traditional cuff-based methods. Approach . The proposed method utilizes the feature extraction ability of a convolution neural network to estimate blood pressure (BP) from PPG signals without the need for waveform analysis and signal feature extraction. Main results . The network achieved mean absolute errors and standard deviations of 2.55 ± 3.92 mmHg for systolic BP (SBP), 1.66 ± 2.76 mmHg for diastolic BP (DBP), and 2.52 ± 3.02 mmHg for overall pressure waveform. The results meet the best levels of the protocols of the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Significance . The proposed method shows promise for noninvasive continuous BP monitoring in hospital wards and daily life, which can assist in clinical diagnosis, disease treatment, and rehabilitation., (© 2023 Institute of Physics and Engineering in Medicine.)

Objective. The aim of the study is to investigate the effect of the signal sampling frequency and low-pass filtering on the accuracy of the localisation of the fiducial points of the photoplethysmographic signal (PPG), and thus on the estimation of the blood pressure (i.e. the accuracy of the estimation). Approach. Statistical analysis was performed on 3,799 data samples taken from a publicly available database. Four PPG fiducial points of each sample signal were examined in the study. Main results. Simulation suggests that for noise-free data, cubic spline interpolation causes the sampling frequency (in the considered range of 62.5-500 Hz) to have only limited influence on localisation of the fiducial point. Better results were obtained for the pulse transit time (PTT) than pulse arrival time (PAT) approach. The acceptable filter band depends on the selected fiducial point and PAT or PTT approach. The best results were obtained for the tangent fiducial point. Significance. The presented results make it possible to estimate the minimum requirements for the sampling frequency and filtering of the PPG signal in order to obtain a reliable estimation of blood pressure., (© 2023 Institute of Physics and Engineering in Medicine.)