Physiological measurement最新文献

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 of1.29±6.33mmHg for systolic pressure and1.17±5.78for diastolic pressure. In contrast, N-PPG based inference exhibited inferior performance than N-IABP, presenting1.49±11.82mmHg and0.89±7.27mmHg 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. 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.

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. Exercise monitoring provides valuable insights into the cardio-respiratory health and fitness performance of exercisers. To address the limitations of existing studies that only monitors specific phases of the fitness cycle, this study introduces a novel approach for camera-based monitoring throughout the entire fitness cycle, encompassing the pre-exercise, during-exercise, and post-exercise phases.Approach. Validated video-based algorithms were employed to monitor physiological parameters, including heart rate (HR), HR variability (HRV) (time-domain, frequency-domain and nonlinear-domain metrics), and respiratory rate (RR). Measurements were conducted using a camera positioned in front of a treadmill, along with electrocardiogram (ECG), PPG recorded simultaneously for benchmarking. This work comprised of a total of 36 adult subjects (18 males, 18 females; average age: 21.3 ± 2.8 years), which are categorized into subjects with regular exercise habits (ES) and those without (NS) (ES: 10, NS: 26) based on their performance of this running trial organized in our study.Main results. The results showed that the camera-based system performed well in HR, RR and HRV measurement. In the pre-exercise phase, camera-based monitoring achieved an mean absolute error of 2.74 bpm for RR and 12.19 bpm for HR. HRV parameters, including mean interbeat interval and very low frequency, showed Pearson correlation coefficients of 0.99 and 0.97, respectively, with ECG. Compared to NS, ES exhibited more robust cardio-respiratory functioning, characterized by lower HR during exercise and faster HR recovery during post-exercise. Camera-based monitoring effectively captured these differences in physiological parameters across the fitness cycle.Significance. This study validates the feasibility and effectiveness of camera-based monitoring throughout the full fitness cycle. The findings highlight the contrasting cardio-respiratory responses between ES and NS, emphasizing the potential of camera-based systems in providing comprehensive, non-invasive insights into exercisers' fitness performance and cardiovascular health.The source code and dataset will be made open-source upon the acceptance at this sitehttps://github.com/contactless-healthcare/Camera-based-Monitoring-for-Full-Fitness-Cycle.

Objective.Patients may not always perform a perfect 90° upper limb abduction when doing an abduction, external rotation test for the evaluation of thoracic outlet syndrome (TOS). We aimed to study the vascular responses to three slightly different abduction angles.Approach.We recorded fingertip arterial (A-PPG) and forearm venous (V-PPG) photo-plethysmography in 111 patients referred for suspicion or follow up of TOS. The measurements were made bilaterally during a 30 s surrender position, followed by moving elbows in the frontal plane without changing elbow and hand level to open the costo-clavicular angle (prayer position) to standardize venous results, either: slightly below (<90°), at the same level of (∼90°), or slightly above (>90°) the shoulder level, in a random order.Main results.With abnormal results defined as A-PPG <5%rest and V-PPG < 70%max in the surrender position, 54 of the 222 upper limbs were normal at all three tests. The proportion of abnormal tests decreased with the increase in abduction angle (CochranQ< 0.05), 135 upper limbs showed impaired venous outflow for one (n= 74), two (n= 47) or the three angles (n= 14) without arterial inflow impairment at any of the three tests.Significance.Slight changes from a 'perfect' 90° abduction angle gave unreliable results during elevation, abduction, external rotation stress tests. A venous outflow impairment should probably be considered a physiologic response at <90° abduction.

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, whereVis cerebral arterial blood volume (CaBV), estimated by integrating CBFV over time. Compliance was calculated across three ICP wave frequencies: vasogenicB-waves, respiratoryR-waves, and pulsatile waves.Main results.Compliances were significantly different (p< 0.001) across frequencies, and moderately correlated (r= 0.52 tor= 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 withB-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.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.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.