Physiological Measurement

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.

Objective: photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The multi-scale peak & trough detection (MSPTD) algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of the MSPTD algorithm for PPG beat detection, named MSPTDfast (v.2). Approach. five potential improvements to MSPTD were identified and evaluated on four datasets. MSPTDfast (v.2) was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a high F₁-score. After internal validation, MSPTDfast (v.2) was benchmarked against state-of-the-art beat detection algorithms on four additional datasets. Main results. MSPTDfast (v.2) incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validation MSPTDfast (v.2) was found to have an execution time of between approximately one-third and one-twentieth of MSPTD, and a comparable F₁-score. During benchmarking MSPTDfast (v.2) was found to have the highest F₁-score alongside MSPTD, and amongst one of the lowest execution times with only MSPTDfast (v.1), qppgfast and MMPD (v.2) achieving shorter execution times. Significance. MSPTDfast (v.2) is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.

Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

Objective: To validate the accuracy of the Oura ring in the quantification of resting heart rate (HR) and heart rate variability (HRV). Background: Wearable devices have become comfortable, lightweight, and technologically advanced for assessing health behavior. As an example, the novel Oura ring integrates daily physical activity and nocturnal cardiovascular measurements. Ring users can follow their autonomic nervous system responses to their daily behavior based on nightly changes in HR and HRV, and adjust their behavior accordingly after self-reflection. As wearable photoplethysmogram (PPG) can be disrupted by several confounding influences, it is crucial to demonstrate the accuracy of ring measurements. Approach: Nocturnal HR and HRV were assessed in 49 adults with simultaneous measurements from the Oura ring and the gold standard ECG measurement. Female and male participants with a wide age range (15–72 years) and physical activity status were included. Regression analysis between ECG and the ring outcomes was performed. Main results: Very high agreement between the ring and ECG was observed for nightly average HR and HRV (r² = 0.996 and 0.980, respectively) with a mean bias of −0.63 bpm and −1.2 ms. High agreement was also observed across 5 min segments within individual nights in (r² = 0.869 ± 0.098 and 0.765 ± 0.178 in HR and HRV, respectively). Significance: Present findings indicate high validity of the Oura ring in the assessment of nocturnal HR and HRV in healthy adults. The results show the utility of this miniaturised device as a lifestyle management tool in long-term settings. High quality PPG signal results prompt future studies utilizing ring PPG towards clinically relevant health outcomes.

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, denoted pyPPG, 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, pyPPG engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on https://physiozoo.com/.

Objective. Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are crucial for diagnosing them. Traditionally, ECGs are stored in printed formats. However, these printouts, even when scanned, are incompatible with advanced ECG diagnosis software that require time-series data. Digitizing ECG images is vital for training machine learning models in ECG diagnosis, leveraging the extensive global archives collected over decades. Deep learning models for image processing are promising in this regard, although the lack of clinical ECG archives with reference time-series data is challenging. Data augmentation techniques using realistic generative data models provide a solution. Approach. We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts from time-series data, aimed at automating the conversion of scanned ECG images to ECG data points. The tool synthesizes ECG images from real time-series data, applying distortions like text artifacts, wrinkles, and creases on a standard ECG paper background. Main results. As a case study, we used ECG-Image-Kit to create a dataset of 21 801 ECG images from the PhysioNet QT database. We developed and trained a combination of a traditional computer vision and deep neural network model on this dataset to convert synthetic images into time-series data for evaluation. We assessed digitization quality by calculating the signal-to-noise ratio and compared clinical parameters like QRS width, RR, and QT intervals recovered from this pipeline, with the ground truth extracted from ECG time-series. The results show that this deep learning pipeline accurately digitizes paper ECGs, maintaining clinical parameters, and highlights a generative approach to digitization. Significance. The toolbox has broad applications, including model development for ECG image digitization and classification. The toolbox currently supports data augmentation for the 2024 PhysioNet Challenge, focusing on digitizing and classifying paper ECG images.

Objective. Large artery stiffening leads to an increase in cardiovascular risk and organ damage of the kidneys, brain or the heart. Biomarkers that allow for early detection of this phenomenon are a point of interest in research, with pulse-wave velocity (PWV) having been proven useful in predicting and monitoring arterial stiffness. We previously introduced a laser Doppler vibrometry (LDV) prototype which can measure carotid–femoral PWV (cfPWV). In this work, we assess the feasibility of using the same device to infer heart-carotid pulse-transit time (hcPTT) as a first step towards measuring heart-carotid PWV (hcPWV). The advantage of hcPWV over cfPWV is that the ascending aorta, which is the most distensible segment of the aorta contributing most to total arterial compliance, is included in the arterial pathway. Approach. Signals were simultaneously acquired from a location on the chest (near either the base or the apex of the heart) and the right carotid artery for 100 patients (45% female). Fiducial points on the heart waveforms are associated with opening and closure (second heart sound; S2) of the aortic valve, which can be combined with, respectively, the foot and dicrotic notch (DN) of the carotid waveform to retrieve hcPTT. Considering two distinct heart-signal measurement sites, four hcPTT estimations are evaluated in about 94% of all measurements. Main results. Correlations between these and known predictors of arterial stiffness i.e. age, blood pressure and carotid–femoral PTT via applanation tonometry indicated that combining S2 from a heart-measurement site located at the base of the heart, with the carotid DN yields hcPTT providing convincing correlations with known determinants of arterial stiffness (ρ = 0.377 with age). Significance. We conclude that LDV may provide a corollary biomarker of arterial stiffness, encompassing the ascending aorta.

Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

Objective. Due to the growing demand for personal health monitoring in extreme environments, continuous monitoring of core temperature has become increasingly important. Traditional monitoring methods, such as mercury thermometers and infrared thermometers, may have limitations in tracking real-time fluctuations in core temperature, especially in special application scenarios such as firefighting, military, and aerospace. This study aims to develop a non-invasive, continuous core temperature prediction model based on machine learning, addressing the limitations of traditional methods in extreme environments. Approach. This study develops a novel machine learning-based non-invasive continuous body core temperature monitoring model. A wearable dual temperature sensing device is designed to collect skin and environment temperature, six machine learning algorithms are trained utilizing data from 62 subjects. Main results. Performance evaluations on a test set of 10 subjects reveal outstanding results, achieving a mean absolute error of 0.15 °C ± 0.04 °C, a root mean square error of 0.17 °C ± 0.05 °C, and a mean absolute percentage error of 0.40% ± 0.12%. Statistical analysis further confirms the model's superior predictive capability compared to traditional methods. Significance. The developed temperature monitoring model not only provides enhanced accuracy in various conditions but also serves as a robust tool for individual health monitoring. This innovation is particularly significant in scenarios requiring continuous and precise temperature tracking, and offering entirely new insights for improved health management strategies and outcomes.

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

Objective. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. While machine learning models can improve classification accuracy, they often lack interpretability, making it difficult to understand how they arrive at their decisions. Approach. Explainable machine learning helps address this issue by identifying which features contribute most to the model's predictions. In this study, we introduce a novel framework leveraging SHapley Additive exPlanations (SHAPs) to identify crucial FNC features distinguishing between two distinct population classes. Main results. Initially, we validate our approach using synthetic data. Subsequently, applying our framework, we ascertain FNC biomarkers distinguishing between, controls and schizophrenia (SZ) patients with accuracy of 81.04% as well as middle aged adults and old aged adults with accuracy 71.38%, respectively, employing random forest, XGBoost, and CATBoost models. Significance. Our analysis underscores the pivotal role of the cognitive control network (CCN), subcortical network (SCN), and somatomotor network in discerning individuals with SZ from controls. In addition, our platform found CCN and SCN as the most important networks separating young adults from older.

Objective. To determine whether historical behavior data can predict the occurrence of high-risk behavioral or Seizure events in individuals with profound Autism Spectrum Disorder (ASD), thereby facilitating early intervention and improved support. Approach. We conducted an analysis of nine years of behavior and seizure data from 353 individuals with ASD. Our analysis focused on the seven most common behaviors labeled by a human, while all other behaviors were grouped into an 'other' category, resulting in a total of eight behavior categories. Using a deep learning algorithm, we predicted the occurrence of seizures and high-risk behavioral events for the following day based on data collected over the most recent 14 d period. We employed permutation-based statistical tests to assess the significance of our predictive performance. Main results. Our model achieved accuracies of 70.5% for seizures, 78.3% for aggression, 80.2% for SIB, and 85.7% for elopement. All results were significant for more than 85% of the population. These findings suggest that high-risk behaviors can serve as early indicators not only of subsequent challenging behaviors but also of upcoming seizure events. Significance. By demonstrating, for the first time, that behavioral patterns can predict seizures as well as adverse behaviors, this approach expands the clinical utility of predictive modeling in ASD. Early warning systems derived from these predictions can guide timely interventions, enhance inclusion in educational and community settings, and improve quality of life by helping anticipate and mitigate severe behavioral and medical events.

Objective. Large artery stiffening leads to an increase in cardiovascular risk and organ damage of the kidneys, brain or the heart. Biomarkers that allow for early detection of this phenomenon are a point of interest in research, with pulse-wave velocity (PWV) having been proven useful in predicting and monitoring arterial stiffness. We previously introduced a laser Doppler vibrometry (LDV) prototype which can measure carotid–femoral PWV (cfPWV). In this work, we assess the feasibility of using the same device to infer heart-carotid pulse-transit time (hcPTT) as a first step towards measuring heart-carotid PWV (hcPWV). The advantage of hcPWV over cfPWV is that the ascending aorta, which is the most distensible segment of the aorta contributing most to total arterial compliance, is included in the arterial pathway. Approach. Signals were simultaneously acquired from a location on the chest (near either the base or the apex of the heart) and the right carotid artery for 100 patients (45% female). Fiducial points on the heart waveforms are associated with opening and closure (second heart sound; S2) of the aortic valve, which can be combined with, respectively, the foot and dicrotic notch (DN) of the carotid waveform to retrieve hcPTT. Considering two distinct heart-signal measurement sites, four hcPTT estimations are evaluated in about 94% of all measurements. Main results. Correlations between these and known predictors of arterial stiffness i.e. age, blood pressure and carotid–femoral PTT via applanation tonometry indicated that combining S2 from a heart-measurement site located at the base of the heart, with the carotid DN yields hcPTT providing convincing correlations with known determinants of arterial stiffness (ρ = 0.377 with age). Significance. We conclude that LDV may provide a corollary biomarker of arterial stiffness, encompassing the ascending aorta.

Objetive. Hypertension, a leading contributor to cardiovascular morbidity, underscores the need for accurate and continuous blood pressure (BP) monitoring. Photoplethysmography (PPG) emerges as a promising approach for continuous BP monitoring. However, the precision of BP estimates derived from PPG signals has been the subject of ongoing debate, requiring a comprehensive evaluation of their efficacy. This paper aims to provide the potentials and limitations regarding BP estimation from single-site PPG signals. Approach. We developed a calibration-based Siamese ResNet model for BP estimation. We compared the use of normalized PPG (N-PPG) against the normalized invasive arterial BP (N-IABP) signals as input. N-IABP signals, while not directly presenting systolic (SBP) and diastolic (DBP) BP values, are expected to offer more precise estimations than PPG since it is a direct pressure sensor inside the body. Thus, if N-IABP poses challenges in BP estimation, predicting BP from PPG signals might be even more challenging. Main results. Our evaluation, conducted using the AAMI and BHS standards on the VitalDB dataset, revealed that inference using N-IABP signals meet with AAMI standards for both SBP and DBP, with errors of $1.29\pm6.33$ mmHg for systolic pressure and $1.17\pm5.78$ for diastolic pressure. In contrast, N-PPG based inference exhibited inferior performance than N-IABP, presenting $1.49\pm11.82$ mmHg and $0.89\pm7.27$ mmHg for systolic and diastolic pressure respectively in their best setup. Significance. Our findings establish a critical benchmark for PPG performance, providing realistic expectations for its BP estimation capabilities. We concluded that while PPG signals contain BP-correlated information, they may not suffice for accurate prediction.

Objective. This study examines the influence on balance regulation of a training program of targeted coordination exercises to improve balance skills in preschool children between the ages of 3 and 7 (in German 'Kindergarten'). On average, the children received targeted, age-appropriate training in basic coordination over a period of 3–4 years during their preschool years. The present results consider selected measurements of balance skills in 5- to 7-year-old children at the end or in the last third of the intervention period. It aims to determine if structured training programs can significantly improve postural control and serve as early interventions for enhancing motor skills. Approach. A cohort of 136 children participated in weekly two-hour coordination training over three years, focusing on foundational motor skills, including balance and spatial orientation. Postural control was measured using the Leonardo Mechanograph® GRFP LT force plate system, employing both linear and nonlinear analyses. The experimental group's performance was compared to a control group of 86 children who did not receive targeted training. Main Results. The experimental group exhibited significant improvements in balance regulation, reflected in steadier posture and reduced fluctuations (p < 0.01). Nonlinear analysis revealed increased stability and frequent occurrence of stationary balance phases. Linear discriminant analysis showed moderate separability (AUC = 0.69) between groups based on balance parameters. The findings underscore the role of intensive, targeted coordination training in enhancing neurophysiological modulation of postural control. Significance. The study highlights the potential of early, structured motor skill programs to address declining physical activity trends and improve holistic child development. These interventions could play a critical role in promoting health, preventing postural issues, and supporting cognitive and motor development in early childhood.

Objective. Wearable technology like the Apple Watch is increasingly important for monitoring health metrics. Accurate measurement is crucial, as inaccuracies can impact health outcomes. Despite extensive research, findings on the Apple Watch's accuracy vary across different conditions. While previous reviews have summarized findings, few have utilized a meta-analytic approach. This study aims to quantitatively evaluate the accuracy of the Apple Watch in measuring health metrics. The accuracy of the Apple Watch was assessed in measuring energy expenditure (EE), heart rate (HR), and step counts (steps). Approach. We searched Embase, PubMed, Scopus, and SPORTDiscus for studies on adults using the Apple Watch compared to reference measures. The Bland–Altman framework was applied to assess mean bias and limits of agreement (LoA), with robust variance estimation to address within-study correlations. Heterogeneity was assessed across variables such as age, health status, device series, activity intensity, and activity type. Additionally, the mean absolute percentage error (MAPE) reported in the included studies was summarized by subgroups. Main results. This review included 56 studies, comprising 270 effect sizes on EE (71), HR (148), and steps (51). The meta-analysis showed a mean bias of 0.30 (LoA: −2.09–2.69) for EE (kcal min⁻¹), −0.12 (LoA: −11.06–10.81) for HR (beats min⁻¹), −1.83 (LoA: −9.08–5.41) for steps (steps min⁻¹). The forest plots showed variability in LoA across subgroups. For MAPE, all subgroups for EE exceeded the 10% validity threshold, while none of the subgroups for HR exceeded this threshold. For steps, some subgroups exceeded 10%, highlighting variability in accuracy based on different conditions. Significance. This study demonstrates that while the Apple Watch generally provides accurate HR and step measurements, its accuracy for EE is limited. Although HR and step measurements showed acceptable accuracy, variability was observed across different user characteristics and measurement conditions. These findings highlight the importance of considering such factors when evaluating validity.

Background. Sudden cardiac death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. It ranks prominently among the leading causes of global mortality, contributing to approximately 10% of deaths worldwide. The timely anticipation of SCD holds the promise of immediate life-saving interventions, such as cardiopulmonary resuscitation. However, recent strides in the realms of deep learning (DL), machine learning (ML), and artificial intelligence have ushered in fresh opportunities for the automation of SCD prediction using physiological signals. Researchers have devised numerous models to automatically predict SCD using a combination of diverse feature extraction techniques and classifiers. Methods: We conducted a thorough review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD. Traditionally, specialists utilize molecular biomarkers, symptoms, and 12-lead ECG recordings for SCD prediction. However, continuous patient monitoring by experts is impractical, and only a fraction of patients seeks help after experiencing symptoms. However, over the past two decades, ML techniques have emerged and evolved for this purpose. Importantly, since 2021, the studies we have scrutinized delve into a diverse array of ML and DL algorithms, encompassing K-nearest neighbors, support vector machines, decision trees, random forest, Naive Bayes, and convolutional neural networks as classifiers. Results. This literature review presents a comprehensive analysis of ML and DL models employed in predicting SCD. The analysis provided valuable information on the fundamental structure of cardiac fatalities, extracting relevant characteristics from electrocardiogram (ECG) and heart rate variability (HRV) signals, using databases, and evaluating classifier performance. The review offers a succinct yet thorough examination of automated SCD prediction methodologies, emphasizing current constraints and underscoring the necessity for further advancements. It serves as a valuable resource, providing valuable insights and outlining potential research directions for aspiring scholars in the domain of SCD prediction. Conclusions. In recent years, researchers have made substantial strides in the prediction of SCD by leveraging openly accessible databases such as the MIT-BIH SCD Holter and Normal Sinus Rhythm, which contains extensive 24 h recordings of SCD patients. These sophisticated methodologies have previously demonstrated the potential to achieve remarkable accuracy, reaching levels as high as 97%, and can forecast SCD events with a lead time of 30–70 min. Despite these promising outcomes, the quest for even greater accuracy and reliability persists. ML and DL methodologies have shown great promise, their performance is intrinsically linked to the volume of training data available. Most predictive models rely on small-scale databases, raising concerns about their applicability in real-world scenarios. Furthermore, these models predominantly utilize ECG and HRV signals, often overlooking the potential contributions of other physiological signals. Developing real-time, clinically applicable models also represents a critical avenue for further exploration in this field.

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

Objective. Intracranial pressure measurement (ICP) is an essential component of deriving of multivariate data metrics foundational to improving understanding of high temporal relationships in cerebral physiology. A significant barrier to this work is artifact ridden data. As such, the objective of this review was to examine the existing literature pertinent to ICP artifact management. Methods. A search of five databases (BIOSIS, SCOPUS, EMBASE, PubMed, and Cochrane Library) was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines with the PRISMA Extension for Scoping Review. The search question examined the methods for artifact management for ICP signals measured in human/animals. Results. The search yielded 5875 unique results. There were 19 articles included in this review based on inclusion/exclusion criteria and article references. Each method presented was categorized as: (1) valid ICP pulse detection algorithms and (2) ICP artifact identification and removal methods. Machine learning-based and filter-based methods indicated the best results for artifact management; however, it was not possible to elucidate a single most robust method. Conclusion. There is a significant lack of standardization in the metrics of effectiveness in artifact removal which makes comparison difficult across studies. Differences in artifacts observed on patient neuropathological health and recording methodologies have not been thoroughly examined and introduce additional uncertainty regarding effectiveness. Significance. This work provides critical insights into existing literature pertaining to ICP artifact management as it highlights holes in the literature that need to be adequately addressed in the establishment of robust artifact management methodologies.

The radial artery, one of the terminal branches of the forearm, is utilized for vascular access and in various non-invasive measurement method, providing crucial medical insights. Various sensor technologies have been developed, each suited to specific characterization requirements. The work presented in this paper is based on a systematic literature review of the main publications relating to this topic. Analysis of the forearm vascular system complex array of anatomical structures shows that the radial artery can be characterized by its size, position, elasticity, tissue evaluation, blood flow and blood composition. The survey of medical procedures for patient monitoring, diagnosis and pre-operative validation shows the use of measures for pulse wave, blood pressure, heart rate, skin temperature, tissue response,$\ldots$ By exploring sensor technologies used for artery characterization, we produce a synthesis of measurement principles, measured phenomena and measurement accuracy for capacitive, piezoresistive, bioimpedance, thermography, fiber optic based, piezoelectric and photoacoustic sensors. A comparative study is conducted for sensor technologies by considering the metrics of the information to be collected and the associated accuracy as well as the portability, the complexity of the processing, the cost and the mode of contact with the arm. Finally, a comprehensive framework is proposed to facilitate informed decisions in the development of medical devices tailored to specific characterization needs.

Objective: Radiofrequency (RF) catheter ablation is a standard treatment for patients with cardiac arrhythmias, providing an efficient, minimally invasive solution. However, the ablation efficiency remains suboptimal due to numerous contributed factors that are overlooked in the literature and not monitored during the procedure. This paper explores the effect of catheter-to-tissue contact angles on lesion formations and the feasibility of the multichannel bioimpedance method in characterising the angles to inform cardiologists. &#xD;Approach: Two silico simulations based on a realistic human model were built to: (1) simulate lesion formations with different catheter-to-tissue angles under varying conditions of powers and convection cooling, and (2) simulate multichannel bioimpedances measured at each catheter's location and angle. 13 locations were picked in all four chambers with 3 contact conditions (catheter lies along the muscle (0° and 180°), in perpendicular to the muscle (90°) and in middle angles (45° and 135°)). 64 electrodes divided into 4 bands were placed on the thorax for multichannel bioimpedances (3-terminal) measured between the catheter's second electrode E2 (I+, V+), and each pair of adjacent surface electrodes (I-,V-). ANOVA and Tukey's Honestly Significant Difference (HSD) tests were used to evaluate the contact angle's effect on the lesion formations and the bioimpedance's capability in distinguishing between angles.&#xD;Main results: The results showed that 0° and 180° configurations generated significantly different lesions from other angles. The multichannel bioimpedances could recognise 0°/ 180° from other angles and correlated moderately to lesion sizes at low ablation power. &#xD;Significance: This paper concludes that catheter-to-tissue angles can influence the lesion outcomes significantly and the multichannel bioimpedance is able to detect the angles that matter.&#xD;

Objective. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. While machine learning models can improve classification accuracy, they often lack interpretability, making it difficult to understand how they arrive at their decisions. Approach. Explainable machine learning helps address this issue by identifying which features contribute most to the model's predictions. In this study, we introduce a novel framework leveraging SHapley Additive exPlanations (SHAPs) to identify crucial FNC features distinguishing between two distinct population classes. Main results. Initially, we validate our approach using synthetic data. Subsequently, applying our framework, we ascertain FNC biomarkers distinguishing between, controls and schizophrenia (SZ) patients with accuracy of 81.04% as well as middle aged adults and old aged adults with accuracy 71.38%, respectively, employing random forest, XGBoost, and CATBoost models. Significance. Our analysis underscores the pivotal role of the cognitive control network (CCN), subcortical network (SCN), and somatomotor network in discerning individuals with SZ from controls. In addition, our platform found CCN and SCN as the most important networks separating young adults from older.

Objective. To determine whether historical behavior data can predict the occurrence of high-risk behavioral or Seizure events in individuals with profound Autism Spectrum Disorder (ASD), thereby facilitating early intervention and improved support. Approach. We conducted an analysis of nine years of behavior and seizure data from 353 individuals with ASD. Our analysis focused on the seven most common behaviors labeled by a human, while all other behaviors were grouped into an 'other' category, resulting in a total of eight behavior categories. Using a deep learning algorithm, we predicted the occurrence of seizures and high-risk behavioral events for the following day based on data collected over the most recent 14 d period. We employed permutation-based statistical tests to assess the significance of our predictive performance. Main results. Our model achieved accuracies of 70.5% for seizures, 78.3% for aggression, 80.2% for SIB, and 85.7% for elopement. All results were significant for more than 85% of the population. These findings suggest that high-risk behaviors can serve as early indicators not only of subsequent challenging behaviors but also of upcoming seizure events. Significance. By demonstrating, for the first time, that behavioral patterns can predict seizures as well as adverse behaviors, this approach expands the clinical utility of predictive modeling in ASD. Early warning systems derived from these predictions can guide timely interventions, enhance inclusion in educational and community settings, and improve quality of life by helping anticipate and mitigate severe behavioral and medical events.

Objective: Radiofrequency (RF) catheter ablation is a standard treatment for patients with cardiac arrhythmias, providing an efficient, minimally invasive solution. However, the ablation efficiency remains suboptimal due to numerous contributed factors that are overlooked in the literature and not monitored during the procedure. This paper explores the effect of catheter-to-tissue contact angles on lesion formations and the feasibility of the multichannel bioimpedance method in characterising the angles to inform cardiologists. &#xD;Approach: Two silico simulations based on a realistic human model were built to: (1) simulate lesion formations with different catheter-to-tissue angles under varying conditions of powers and convection cooling, and (2) simulate multichannel bioimpedances measured at each catheter's location and angle. 13 locations were picked in all four chambers with 3 contact conditions (catheter lies along the muscle (0° and 180°), in perpendicular to the muscle (90°) and in middle angles (45° and 135°)). 64 electrodes divided into 4 bands were placed on the thorax for multichannel bioimpedances (3-terminal) measured between the catheter's second electrode E2 (I+, V+), and each pair of adjacent surface electrodes (I-,V-). ANOVA and Tukey's Honestly Significant Difference (HSD) tests were used to evaluate the contact angle's effect on the lesion formations and the bioimpedance's capability in distinguishing between angles.&#xD;Main results: The results showed that 0° and 180° configurations generated significantly different lesions from other angles. The multichannel bioimpedances could recognise 0°/ 180° from other angles and correlated moderately to lesion sizes at low ablation power. &#xD;Significance: This paper concludes that catheter-to-tissue angles can influence the lesion outcomes significantly and the multichannel bioimpedance is able to detect the angles that matter.&#xD;

Objective. Large artery stiffening leads to an increase in cardiovascular risk and organ damage of the kidneys, brain or the heart. Biomarkers that allow for early detection of this phenomenon are a point of interest in research, with pulse-wave velocity (PWV) having been proven useful in predicting and monitoring arterial stiffness. We previously introduced a laser Doppler vibrometry (LDV) prototype which can measure carotid–femoral PWV (cfPWV). In this work, we assess the feasibility of using the same device to infer heart-carotid pulse-transit time (hcPTT) as a first step towards measuring heart-carotid PWV (hcPWV). The advantage of hcPWV over cfPWV is that the ascending aorta, which is the most distensible segment of the aorta contributing most to total arterial compliance, is included in the arterial pathway. Approach. Signals were simultaneously acquired from a location on the chest (near either the base or the apex of the heart) and the right carotid artery for 100 patients (45% female). Fiducial points on the heart waveforms are associated with opening and closure (second heart sound; S2) of the aortic valve, which can be combined with, respectively, the foot and dicrotic notch (DN) of the carotid waveform to retrieve hcPTT. Considering two distinct heart-signal measurement sites, four hcPTT estimations are evaluated in about 94% of all measurements. Main results. Correlations between these and known predictors of arterial stiffness i.e. age, blood pressure and carotid–femoral PTT via applanation tonometry indicated that combining S2 from a heart-measurement site located at the base of the heart, with the carotid DN yields hcPTT providing convincing correlations with known determinants of arterial stiffness (ρ = 0.377 with age). Significance. We conclude that LDV may provide a corollary biomarker of arterial stiffness, encompassing the ascending aorta.

Objective. This study examines the influence on balance regulation of a training program of targeted coordination exercises to improve balance skills in preschool children between the ages of 3 and 7 (in German 'Kindergarten'). On average, the children received targeted, age-appropriate training in basic coordination over a period of 3–4 years during their preschool years. The present results consider selected measurements of balance skills in 5- to 7-year-old children at the end or in the last third of the intervention period. It aims to determine if structured training programs can significantly improve postural control and serve as early interventions for enhancing motor skills. Approach. A cohort of 136 children participated in weekly two-hour coordination training over three years, focusing on foundational motor skills, including balance and spatial orientation. Postural control was measured using the Leonardo Mechanograph® GRFP LT force plate system, employing both linear and nonlinear analyses. The experimental group's performance was compared to a control group of 86 children who did not receive targeted training. Main Results. The experimental group exhibited significant improvements in balance regulation, reflected in steadier posture and reduced fluctuations (p < 0.01). Nonlinear analysis revealed increased stability and frequent occurrence of stationary balance phases. Linear discriminant analysis showed moderate separability (AUC = 0.69) between groups based on balance parameters. The findings underscore the role of intensive, targeted coordination training in enhancing neurophysiological modulation of postural control. Significance. The study highlights the potential of early, structured motor skill programs to address declining physical activity trends and improve holistic child development. These interventions could play a critical role in promoting health, preventing postural issues, and supporting cognitive and motor development in early childhood.

Objective. Due to the growing demand for personal health monitoring in extreme environments, continuous monitoring of core temperature has become increasingly important. Traditional monitoring methods, such as mercury thermometers and infrared thermometers, may have limitations in tracking real-time fluctuations in core temperature, especially in special application scenarios such as firefighting, military, and aerospace. This study aims to develop a non-invasive, continuous core temperature prediction model based on machine learning, addressing the limitations of traditional methods in extreme environments. Approach. This study develops a novel machine learning-based non-invasive continuous body core temperature monitoring model. A wearable dual temperature sensing device is designed to collect skin and environment temperature, six machine learning algorithms are trained utilizing data from 62 subjects. Main results. Performance evaluations on a test set of 10 subjects reveal outstanding results, achieving a mean absolute error of 0.15 °C ± 0.04 °C, a root mean square error of 0.17 °C ± 0.05 °C, and a mean absolute percentage error of 0.40% ± 0.12%. Statistical analysis further confirms the model's superior predictive capability compared to traditional methods. Significance. The developed temperature monitoring model not only provides enhanced accuracy in various conditions but also serves as a robust tool for individual health monitoring. This innovation is particularly significant in scenarios requiring continuous and precise temperature tracking, and offering entirely new insights for improved health management strategies and outcomes.

Objective. Craniospinal compliance (CC) refers to the ability to maintain stable intracranial pressure (ICP) given changes in intracranial volume. CC can be calculated directly as the change in intracranial volume over change in ICP (ΔV/ΔICP). Considering the distinct spectral components of the ICP signal, it is pertinent to explore whether compliance is dependent on the frequency at which it is calculated. Approach. Data from 92 hydrocephalus patients who underwent computerized infusion studies was retrospectively analysed. ICP was recorded via lumbar puncture and cerebral blood flow velocity (CBFV) using transcranial Doppler ultrasonography. Compliance was calculated as ΔV/ΔICP, where V is cerebral arterial blood volume (CaBV), estimated by integrating CBFV over time. Compliance was calculated across three ICP wave frequencies: vasogenic B-waves, respiratory R-waves, and pulsatile waves. Main results. Compliances were significantly different (p < 0.001) across frequencies, and moderately correlated (r = 0.52 to r = 0.66), during baseline and plateau phases of the infusion study. Compliance decreased significantly from baseline to plateau (p < 0.001). B-wave CaBV amplitude was significantly higher than all other frequencies during both phases (p < 0.001), while pulsatile ICP amplitude was highest at baseline (p < 0.01), but tied with B-wave ICP amplitude during plateau (p = 0.10). Significance. The results support the notion that compliance is dependent on frequency, with higher compliances at slower frequencies. Where compliance is calculated in a clinical context, in hydrocephalus and traumatic brain injury, frequency should be considered for accurate results. Further research should explore this in a larger cohort, and in additional pathologies.

Objective. Electrical impedance tomography (EIT) has shown the ability to provide clinically useful functional information on ventilation in humans and other land mammals. We are motivated to use EIT with sea mammals and human divers, since EIT could provide unique information on lung ventilation that can help address diver performance and safety, and veterinary and behavioral questions. However, in-water use of EIT is challenging, primarily because sea water is more conductive than the body. Approach. We first address this issue by modeling the in-water component and evaluating image reconstruction algorithms. Main results. EIT is able to produce reasonable images if an outer insulating layer allows a water layer thickness <2% of the body radius. We next describe the design of custom EIT belts with an outer neoprene insulator to minimize current leakage. We show example underwater EIT recordings in human and dolphin subjects. Significance. We demonstrate in-water EIT is feasible with appropriate techniques.

Objective. Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the-art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device. Methods. We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease. Results. The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient of r = 0.73, a mean absolute percentage error of 23% and a bias of −0.08. Conclusions. These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy. Significance. The benefit of a mobile ECG-based solution for measuring FEV1 is that it would require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.

Objective. Respiratory rate (RR) is an important vital sign but is often neglected. Multiple technologies exist for RR monitoring but are either expensive or impractical. Tri-axial accelerometry represents a minimally intrusive solution for continuous RR monitoring, however, the method has not been validated in a wide RR range. Therefore, the aim of this study was to investigate the agreement between RR estimation from a tri-axial accelerometer and a reference method in a wide RR range. Approach. Twenty-five healthy participants were recruited. For accelerometer RR estimation, the accelerometer was placed on the abdomen for optimal breathing movement detection. The acquired accelerometry data were processed using a lowpass filter, principal component analysis (PCA), and autocorrelation. The subjects were instructed to breathe at slow, normal, and fast paces in segments of 60 s. A flow meter was used as reference. Furthermore, the PCA-autocorrelation method was compared with a similar single axis method. Main results. The PCA-autocorrelation method resulted in a bias of 0.0 breaths per minute (bpm) and limits of agreement (LOA) = [−1.9; 1.9 bpm] compared to the reference. Overall, 99% of the RRs estimated by the PCA-autocorrelation method were within ±2 bpm of the reference. A Pearson correlation indicated a very strong correlation with r = 0.99 ($p \lt$0.001). The single axis method resulted in a bias of 3.7 bpm, LOA = [−14.9; 22.3 bpm], and r = 0.44 ($p \lt$0.001). Significance. The results indicate a strong agreement between the PCA-autocorrelation method and the reference. Furthermore, the PCA-autocorrelation method outperformed the single axis method.