The feasibility of population screening for paroxysmal atrial fibrillation using hand-held electrocardiogram devices.

AIMS: There are few data on the feasibility of population screening for paroxysmal atrial fibrillation (AF) using hand-held electrocardiogram (ECG) devices outside a specialist setting or in people over the age of 75. We investigated the feasibility of screening when conducted without face-to-face contact ('remote') or via in-person appointments in primary care and explored impact of age on screening outcomes. METHODS AND RESULTS: People aged ≥65 years from 13 general practices in England participated in screening during 2019-20. This involved attending a practice nurse appointment (10 practices) or receiving an ECG device by post (three practices). Participants were asked to use a hand-held ECG for 1-4 weeks. Screening outcomes included uptake, quality of ECGs, AF detection rates, and uptake of anticoagulation if AF was detected. Screening was carried out by 2141 (87.5%) of people invited to practice nurse-led screening and by 288 (90.0%) invited to remote screening. At least 56 interpretable ECGs were provided by 98.0% of participants who participated for 3 weeks, with no significant differences by setting or age, except people aged 85 or over (91.1%). Overall, 2.6% (64/2429) screened participants had AF, with detection rising with age (9.2% in people aged 85 or over). A total of 53/64 (82.8%) people with AF commenced anticoagulation. Uptake of anticoagulation did not vary by age. CONCLUSION: Population screening for paroxysmal AF is feasible in general practice and without face-to-face contact for all ages over 64 years, including people aged 85 and over.

Reliability of single-lead electrocardiogram interpretation to detect atrial fibrillation: insights from the SAFER feasibility study.

AIMS: Single-lead electrocardiograms (ECGs) can be recorded using widely available devices such as smartwatches and handheld ECG recorders. Such devices have been approved for atrial fibrillation (AF) detection. However, little evidence exists on the reliability of single-lead ECG interpretation. We aimed to assess the level of agreement on detection of AF by independent cardiologists interpreting single-lead ECGs and to identify factors influencing agreement. METHODS AND RESULTS: In a population-based AF screening study, adults aged ≥65 years old recorded four single-lead ECGs per day for 1-4 weeks using a handheld ECG recorder. Electrocardiograms showing signs of possible AF were identified by a nurse, aided by an automated algorithm. These were reviewed by two independent cardiologists who assigned participant- and ECG-level diagnoses. Inter-rater reliability of AF diagnosis was calculated using linear weighted Cohen's kappa (κw). Out of 2141 participants and 162 515 ECGs, only 1843 ECGs from 185 participants were reviewed by both cardiologists. Agreement was moderate: κw = 0.48 (95% confidence interval, 0.37-0.58) at participant level and κw = 0.58 (0.53-0.62) at ECG level. At participant level, agreement was associated with the number of adequate-quality ECGs recorded, with higher agreement in participants who recorded at least 67 adequate-quality ECGs. At ECG level, agreement was associated with ECG quality and whether ECGs exhibited algorithm-identified possible AF. CONCLUSION: Inter-rater reliability of AF diagnosis from single-lead ECGs was found to be moderate in older adults. Strategies to improve reliability might include participant and cardiologist training and designing AF detection programmes to obtain sufficient ECGs for reliable diagnoses.

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

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

Noninvasive hemodynamic indices of vascular aging: an in silico assessment.

Vascular aging (VA) involves structural and functional changes in blood vessels that contribute to cardiovascular disease. Several noninvasive pulse wave (PW) indices have been proposed to assess the arterial stiffness component of VA in the clinic and daily life. This study investigated 19 of these indices, identified in recent review articles on VA, by using a database comprising 3,837 virtual healthy subjects aged 25-75 yr, each with unique PW signals simulated under various levels of artificial noise to mimic real measurement errors. For each subject, VA indices were calculated from filtered PW signals and compared with the precise theoretical value of aortic Young's modulus (EAo). In silico PW indices showed age-related changes that align with in vivo population studies. The cardio-ankle vascular index (CAVI) and all pulse wave velocity (PWV) indices showed strong linear correlations with EAo (Pearson's rp > 0.95). Carotid distensibility showed a strong negative nonlinear correlation (Spearman's rs < -0.99). CAVI and distensibility exhibited greater resilience to noise compared with PWV indices. Blood pressure-related indices and photoplethysmography (PPG)-based indices showed weaker correlations with EAo (rp and rs < 0.89, |rp| and |rs| < 0.84, respectively). Overall, blood pressure-related indices were confounded by more cardiovascular properties (heart rate, stroke volume, duration of systole, large artery diameter, and/or peripheral vascular resistance) compared with other studied indices, and PPG-based indices were most affected by noise. In conclusion, carotid-femoral PWV, CAVI and carotid distensibility emerged as the superior clinical VA indicators, with a strong EAo correlation and noise resilience. PPG-based indices showed potential for daily VA monitoring under minimized noise disturbances.NEW & NOTEWORTHY For the first time, 19 noninvasive pulse wave indices for assessing vascular aging were examined together in a single database of nearly 4,000 subjects aged 25-75 yr. The dataset contained precise values of the aortic Young's modulus and other hemodynamic measures for each subject, which enabled us to test each index's ability to measure changes in aortic stiffness while accounting for confounding factors and measurement errors. The study provides freely available tools for analyzing these and additional indices.

Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet.

Arterial pulse waves (PWs) such as blood pressure and photoplethysmogram (PPG) signals contain a wealth of information on the cardiovascular (CV) system that can be exploited to assess vascular age and identify individuals at elevated CV risk. We review the possibilities, limitations, complementarity, and differences of reduced-order, biophysical models of arterial PW propagation, as well as theoretical and empirical methods for analyzing PW signals and extracting clinically relevant information for vascular age assessment. We provide detailed mathematical derivations of these models and theoretical methods, showing how they are related to each other. Finally, we outline directions for future research to realize the potential of modeling and analysis of PW signals for accurate assessment of vascular age in both the clinic and in daily life.

An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening.

AIMS: Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG. METHODS AND RESULTS: A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis. The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence-based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78-0.83], which had a wide age range of 65-90+ years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75-76 years), with AUCs of 0.62 (CI 0.61-0.64) and 0.62 (CI 0.58-0.65), respectively. CONCLUSION: An artificial intelligence-enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.

Inaccuracy of pulse oximetry with dark skin pigmentation: clinical implications and need for improvement.

Recent reports highlight potential inaccuracies of pulse oximetry in patients with various degrees of skin pigmentation. We summarise the literature, provide an overview of potential clinical implications, and provide insights into how pulse oximetry could be improved to mitigate against such potential shortcomings.

Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography.

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet.

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

Wearable Photoplethysmography for Cardiovascular Monitoring.

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Relationship between fiducial points on the peripheral and central blood pressure waveforms: rate of rise of the central waveform is a determinant of peripheral systolic blood pressure.

Central systolic blood pressure (cSBP, the peak of the central waveform) is usually regarded as the determinant of peripheral systolic blood pressure with amplification of peripheral systolic blood pressure (pSBP) measured with reference to cSBP. However, the earlier portion of the central waveform up to the first systolic shoulder (P1) may be the major determinant of pSBP. We performed in silico simulation studies and examined previously acquired experimental data (n = 131) in which peripheral and central blood pressure waveforms had been acquired both invasively and noninvasively to examine the determinants of pSBP. Measurements were made at baseline and during perturbation of hemodynamics by inotropic and vasoactive drugs. In silico simulations using a central-to-peripheral transfer function demonstrated that pSBP is dependent on P1 and the rate of change (dP/dt) of central pressure up to the time of P1 but not cSBP. In computational simulations, peripheral reflection in the radial artery was closely related to dP/dt, and 97% of the variability in amplification as measured with reference to P1 was explained by dP/dt. In vivo, amplification of pSBP over P1 was correlated with dP/dt (R > 0.75, P < 0.0001 for all data sets), and P1 and dP/dt were independently correlated with pSBP, explaining 90% of the variability in pSBP. We conclude that P1 and dP/dt are major determinants of pSBP and that pSBP and cSBP are, in part, determined by different cardiac, central, and peripheral vascular properties.NEW & NOTEWORTHY Peripheral systolic BP is determined mainly by the first shoulder and the rate of rise of the central systolic blood pressure waveform rather than the peak of this waveform (central systolic BP). Peripheral and central systolic blood pressure are determined by different cardiac and vascular properties.

Estimating central blood pressure from aortic flow: development and assessment of algorithms.

Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm's performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data.NEW & NOTEWORTHY First, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available.

Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes.

The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25-75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known.NEW & NOTEWORTHY First, a comprehensive literature review of changes in cardiovascular properties with age was performed. Second, an approach for simulating pulse waves (PWs) at different ages was designed and verified against in vivo data. Third, a PW database was created, and its utility was illustrated through three case studies investigating the determinants of PW indexes. Fourth, the database and tools for creating the database, analyzing PWs, and replicating the case studies are freely available.

Molecular chess? Hallmarks of anti-cancer drug resistance.

BACKGROUND: The development of resistance is a problem shared by both classical chemotherapy and targeted therapy. Patients may respond well at first, but relapse is inevitable for many cancer patients, despite many improvements in drugs and their use over the last 40 years. REVIEW: Resistance to anti-cancer drugs can be acquired by several mechanisms within neoplastic cells, defined as (1) alteration of drug targets, (2) expression of drug pumps, (3) expression of detoxification mechanisms, (4) reduced susceptibility to apoptosis, (5) increased ability to repair DNA damage, and (6) altered proliferation. It is clear, however, that changes in stroma and tumour microenvironment, and local immunity can also contribute to the development of resistance. Cancer cells can and do use several of these mechanisms at one time, and there is considerable heterogeneity between tumours, necessitating an individualised approach to cancer treatment. As tumours are heterogeneous, positive selection of a drug-resistant population could help drive resistance, although acquired resistance cannot simply be viewed as overgrowth of a resistant cancer cell population. The development of such resistance mechanisms can be predicted from pre-existing genomic and proteomic profiles, and there are increasingly sophisticated methods to measure and then tackle these mechanisms in patients. CONCLUSION: The oncologist is now required to be at least one step ahead of the cancer, a process that can be likened to 'molecular chess'. Thus, as well as an increasing role for predictive biomarkers to clinically stratify patients, it is becoming clear that personalised strategies are required to obtain best results.

Kinetic and mechanistic characterisation of Choline Kinase-α.

Choline Kinase is a key component of the Kennedy pathway that converts choline into a number of structural and signalling lipids that are essential for cell growth and survival. One member of the family, Choline Kinase-α (ChoKα) is frequently up-regulated in human cancers, and expression of ChoKα is sufficient to transform cells. Consequently ChoKα has been studied as a potential target for therapeutic agents in cancer research. Despite great interest in the enzyme, mechanistic studies have not been reported. In this study, a combination of initial velocity and product inhibition studies, together with the kinetic and structural characterisation of a novel ChoKα inhibitor is used to support a mechanism of action for human ChoKα. Substrate and inhibition kinetics are consistent with an iso double displacement mechanism, in which the γ-phosphate from ATP is transferred to choline in two distinct steps via a phospho-enzyme intermediate. Co-crystal structures, and existing site-specific mutation studies, support an important role for Asp306, in stabilising the phospho-enzyme intermediate. The kinetics also indicate a distinct kinetic (isomerisation) step associated with product release, which may be attributed to a conformational change in the protein to disrupt an interaction between Asp306 and the phosphocholine product, facilitating product release. This study describes a mechanism for ChoKα that is unusual amongst kinases, and highlights the availability of different enzyme states that can be exploited for drug discovery.

Structure of human Bloom's syndrome helicase in complex with ADP and duplex DNA.

Bloom's syndrome is an autosomal recessive genome-instability disorder associated with a predisposition to cancer, premature aging and developmental abnormalities. It is caused by mutations that inactivate the DNA helicase activity of the BLM protein or nullify protein expression. The BLM helicase has been implicated in the alternative lengthening of telomeres (ALT) pathway, which is essential for the limitless replication of some cancer cells. This pathway is used by 10-15% of cancers, where inhibitors of BLM are expected to facilitate telomere shortening, leading to apoptosis or senescence. Here, the crystal structure of the human BLM helicase in complex with ADP and a 3'-overhang DNA duplex is reported. In addition to the helicase core, the BLM construct used for crystallization (residues 640-1298) includes the RecQ C-terminal (RQC) and the helicase and ribonuclease D C-terminal (HRDC) domains. Analysis of the structure provides detailed information on the interactions of the protein with DNA and helps to explain the mechanism coupling ATP hydrolysis and DNA unwinding. In addition, mapping of the missense mutations onto the structure provides insights into the molecular basis of Bloom's syndrome.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis.

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

Photoplethysmogram beat detection using Symmetric Projection Attractor Reconstruction.

Many methods have been proposed to detect beats in photoplethysmogram (PPG) signals. We present a novel method which uses the Symmetric Projection Attractor Reconstruction (SPAR) method to generate an attractor in a two dimensional phase space from the PPG signal. We can then define a line through the origin of this phase space to be a Poincaré section, as is commonly used in dynamical systems. Beats are detected when the attractor trajectory crosses the Poincaré section. By considering baseline drift, we define an optimal Poincaré section to use. The performance of this method was assessed using the WESAD dataset, achieving median F 1 scores of 74.3% in the Baseline phase, 63.0% during Stress, 93.6% during Amusement, and 97.7% during Meditation. Performance was better than an earlier version of the method, and comparable to one of the best algorithms identified in a recent benchmarking study of 15 beat detection algorithms. In addition, our method performed better than two others in the accuracy of the inter-beat intervals for two resting subjects.

Obstructive Sleep Apnea Characterization: A Multimodal Cross-Recurrence-Based Approach for Investigating Atrial Fibrillation.

Obstructive sleep apnea (OSA) is believed to contribute significantly to atrial fibrillation (AF) development in certain patients. Recent studies indicate a rising risk of AF with increasing OSA severity. However, the commonly used apnea-hypopnea index in clinical practice may not adequately account for the potential cardiovascular risks associated with OSA. 1) Objective: to propose and explore a novel method for assessing OSA severity considering potential connection to cardiac arrhythmias. 2) Method: the approach utilizes cross-recurrence features to characterize OSA and AF by considering the relationships among oxygen desaturation, pulse arrival time, and heart-beat intervals. Multinomial logistic regression models were trained to predict four levels of OSA severity and four groups related to heart rhythm issues. The rank biserial correlation coefficient, rrb, was used to estimate effect size for statistical analysis. The investigation was conducted using the MESA database, which includes polysomnography data from 2055 subjects. 3) Results: a derived cross-recurrence-based index showed a significant association with a higher OSA severity (p 0.01) and the presence of AF (p 0.01). Additionally, the proposed index had a significantly larger effect, rrb, than the conventional apnea-hypopnea index in differentiating increasingly severe heart rhythm issue groups: 0.14 0.06, 0.33 0.10, and 0.41 0.07. 4) Significance: the proposed method holds relevance as a supplementary diagnostic tool for assessing the authentic state of sleep apnea in clinical practice.

Advanced waveform analysis of the photoplethysmogram signal using complementary signal processing techniques for the extraction of biomarkers of cardiovascular function.

Introduction: Photoplethysmogram signals from wearable devices typically measure heart rate and blood oxygen saturation, but contain a wealth of additional information about the cardiovascular system. In this study, we compared two signal-processing techniques: fiducial point analysis and Symmetric Projection Attractor Reconstruction, on their ability to extract new cardiovascular information from a photoplethysmogram signal. The aim was to identify fiducial point analysis and Symmetric Projection Attractor Reconstruction indices that could classify photoplethysmogram signals, according to age, sex and physical activity. Methods: Three datasets were used: an in-silico dataset of simulated photoplethysmogram waves for healthy male participants (25-75 years old); an in-vivo dataset containing 10-min photoplethysmogram recordings from 57 healthy subjects at rest (18-39 or > 70 years old; 53% female); and an in-vivo dataset containing photoplethysmogram recordings collected for 4 weeks from a single subject, in daily life. The best-performing indices from the in-silico study (5/48 fiducial point analysis and 6/49 Symmetric Projection Attractor Reconstruction) were applied to the in-vivo datasets. Results: Key fiducial point analysis and Symmetric Projection Attractor Reconstruction indices, which showed the greatest differences between groups, were found to be consistent across datasets. These indices were related to systolic augmentation, diastolic peak positioning and prominence, and waveform variability. Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques provided indices that supported the classification of age and physical activity, but not sex. Conclusions: Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques demonstrated utility in identifying cardiovascular differences between individuals and within an individual over time. Future research should investigate the potential utility of these techniques for extracting information on fitness and disease, to support healthcare-decision making.