Physiological measurement最新文献

Objective.To evaluate the feasibility of seismocardiography (SCG)-based estimation of hemodynamic parameters during submaximal cycle ergometer exercise across different body mass index (BMI) groups.Approach.Sixty healthy adults (n= 15 per BMI group: underweight, normal weight, overweight, obese) performed a YMCA submaximal cycling test while SCG signals were recorded using a chest-mounted accelerometer. Transthoracic bioimpedance (PhysioFlow) served as reference. Time-domain features from tri-axial SCG signals were used in subject-specific random forest regressors to estimate stroke volume (SV), heart rate (HR), cardiac output (CO), and cardiac index. Performance was evaluated across baseline, exercise, and post-exercise phases using the mean absolute percentage error (MAPE) and coefficient of determination (R²).Main results.While SCG signals were successfully acquired across all phases, estimation performance varied significantly by physiological state. Models achieved MAPEs below 8% for all parameters overall. However, model reliability was condition-dependent, with optimal performance during post-exercise recovery (medianR²= 0.75 for HR and CO; 0.42 for SV) with reduced reliability during active cycling. SCG features demonstrated limited sensitivity to BMI variations compared to reference hemodynamic parameters, which may limit personalized estimation accuracy across diverse body compositions.Significance.SCG acquisition is technically viable during exercise, but reliable hemodynamic estimation under high-motion conditions remains limited due to motion artifacts and physiological variability. Post-exercise recovery provides optimal conditions for SCG-based monitoring. SCG shows promise as a lightweight approach for cardiovascular assessment in recovery or low-motion scenarios rather than during active exercise. Further validation using gold-standard methods is warranted.

Objective. Biological signals can be used to record sleep activities and can be used to identify sleep disorders. Insomnia is a sleep disorder that can be detected using supervised learning models developed using biological signal analysis. The baseline insomnia detection models segmented input signals based on various sleep stages, in which an imbalance in classes of the different subsets was visible.Approach. Leaning on sleep annotations for training data generation can overcome using electroencephalogram (EEG) augmentation, which trains the machine learning model based on the diverse nature of input EEG. The proposed work aims to generate a heterogeneity in the decomposed frequencies of EEG data using sub-band augmentation. The presented approach imposes the characteristics of various EEG frequencies when developing new data.Results. An excellent classification accuracy of 0.91, 0.90, and 0.866 can be visible in sub-band augmentation using signal scaling followed by noise addition and sliding window, respectively. An ensemble-bagged decision tree (EBDT) classifier was employed in developing the identification model incorporating all the sub-band augmentations with a significant accuracy of 0.986, a sensitivity of 1.0, and a specificity of 0.97. The proposed model also examines the features from smaller time segments of EEG in developing the training data for EBDT and shows an accuracy, sensitivity, and specificity corresponding to 0.97, 0.95, and 1.0.Significance. The presented model is simple, independent of supplementary data like sleep annotations describing sleep stages, and more suitable for disease detection bearing small datasets in training-data enhancement for classification.

Objective. The accurate assessment of cognitive impairment plays a vital role in more targeted treatments for Dementia. Eye movement analysis is a non-invasive and objective method that offers fine-grained insight into cognitive functioning, complementing conventional screening tools. However, single-task eye-tracking paradigms and simplistic analysis methods limit the potential for comprehensive and fine-grained assessment of cognitive impairment. To address this limitation, we propose a multilevel saccade paradigm combined with differential analysis and an attention-based neural network to enhance eye-tracking-based cognitive impairment assessment.Approach. Firstly, a set of saccade-based paradigms with graded difficulty levels is developed, including prosaccade, antisaccade, and random pro-/antisaccade paradigms. Each paradigm incorporates eye movement assessments in both horizontal and vertical directions. Secondly, we recruit 90 subjects for eye-tracking assessments to build a large-scale dataset. The subjects consisted of 36 healthy young controls, 15 healthy elderly controls, 23 individuals with mild cognitive impairment, and 16 individuals with dementia. Each subject completed the Montreal Cognitive Assessment (MoCA). Third, the Mann-WhitneyUtest is employed to identify eye movement features that show significant differences across the four groups. Correlation analysis with MoCA scores further validated the effectiveness of these eye movement features in distinguishing cognitive impairment. Finally, XGBoost is employed to perform classification and to validate the effectiveness of the eye movement feature selection scheme derived from the difficulty-graded saccade paradigms. An attention-based neural network is also integrated to enhance classification accuracy and improve feature selection by identifying the most informative eye movement features.Main results. The model achieved an area under the receiver operating characteristic curve of 0.94, a classification accuracy of 0.80, and a Matthews correlation coefficient of 0.73. Among all features extracted from the different saccade paradigms, the time to first correct AOI and saccade latency parameters from the random pro-antisaccade paradigm demonstrate the highest contribution to classification performance.Significance. By integrating graded saccade paradigms with statistical analysis and attention neural network, this study enhances the granularity and accuracy of eye-tracking-based cognitive assessment, offering a scalable and non-invasive tool for early detection and monitoring of cognitive decline.

Objective. Chronic hyperglycemia is known to contribute to cerebral microangiopathy via an endothelial dysfunction. We hypothesized that gain, as a marker of vascular compliance or stiffness (as its physical inverse), is associated with an increased HbA1c level.Approach. We conducted a retrospective analysis of 94 consecutive patients (27 females, 67 males; median age 72.5 years, IQR 61-80 years) with isolated acute microangiopathic lacunar infarctions. By selecting this specific patient cohort, we minimized the influence of infarct size on dynamic cerebral autoregulation (dCA). dCA parameters-phase and gain- were assessed using transfer function analysis of spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity in both middle cerebral arteries. HbA1c levels [normal < 5.7% (39 mmol mol^-1), prediabetic 5.7-6.4% (39-46 mmol mol^-1), diabetic ⩾6.5% (>46 mmol mol^-1)], Fazekas grading for small vessel disease was determined on magnet resonance imaging, and other routine diagnostics parameters were recorded.Main results. Neither phase nor gain differed significantly between the Fazekas grading groups. Among the HbA1c categories, phase remained unchanged, whereas gain progressively increased from the normal HbA1c group to the diabetic group significantly in the very low (0.02-0.07 Hz) frequencies (p= .02) and by trend in the low frequency (0.07-0.20 Hz) range (p= .07), while BP and end-tidal carbon dioxide levels were not different across the groups.Significance. In this cohort of patients with microangiopathic lacunar stroke, higher HbA1c levels were associated with increased vascular gain, suggesting a potential link between long-term glucose dysregulation, increased vascular stiffness, and impaired dCA. This finding provides a mechanistic pathway connecting chronic hyperglycaemia to microangiopathic cerebral infarction.

Objective.The aim was to examine the relationship between accelerometer and oxygen consumption (VO₂) metrics and to what extent the metrics are normalized across age and body size, to allow a single calibration regression line for absolute physical activity (PA) intensity.Approach.Hip-mounted accelerometer data and VO₂measurements were collected from 51 participants across five age cohorts (4-5; 6-8; 10; 15 and 20 years) during resting, walking and running on a treadmill in laboratory setting. Linear regressions were used to determine four accelerometer metrics' (AG, 4 Hz frequency extended method (FEM), 10 Hz FEM and Euclidean norm minus one) contribution to explained variance (adjustedR²) in six VO₂metrics (VO₂, VO₂/kg¹, VO₂/kg^0.67, VO₂/kg^0.75, MET_measuredand MET_fixed). Plots were generated for visual representations together with log-linear regression, finding the optimal scaling exponent for VO₂.Main result.10 Hz FEM explained the highest amount of explained variance when related to VO₂/kg^0.75, 92.4%, with minimal remaining between-group and inter-individual variance. The relationship demonstrated a linear shape. The most used accelerometer metric, AG counts, together with traditionally used reference standard, MET_fixed, show substantially lower explained variance, 60.2%, with large between-group and inter-individual variance, insufficiently adjusting for physiological and biomechanical variability. The best body weight scaling factor for VO₂was 0.77. Findings support the use of a single linear calibration regression line for absolute PA intensity across wide-ranging age-groups, accounting for biomechanical and physiological variance.Significance.This enables reliable and meaningful comparisons of PA intensity across age-groups, possibly also across childhood into adulthood, overcoming traditional limitations and enhancing research quality.

Objectives. Impaired cerebral autoregulation could contribute to postpartum stroke risk in individuals with preeclampsia. We modeled aggregated static autoregulatory curves in the postpartum period in individuals with no hypertension, preeclampsia, and chronic hypertension with superimposed preeclampsia.Approach. This is a retrospective analysis of data from a prospective observational study of postpartum participants. We measured continuous mean arterial pressure (MAP) with finger plethysmography and cerebral blood velocity (CBv) with transcranial Doppler within 2 weeks after delivery. Data were aggregated and group curves generated from normalized MAP and CBv data using 3rd order polynomial equations. We compared overall polynomial curve shapes between groups as well as pair-wise comparisons of autoregulatory range.Main results. A total of 73 participants were enrolled: 21 (29%) normotensive, 31 (42%) with preeclampsia and 21 (29%) with superimposed preeclampsia. PolynomialS-curves suggested a flatter plateau in the normotensive group compared with both preeclampsia groups, but the differences were not statistically significant. Autoregulatory range were wider in both preeclampsia groups than in the normotensive group, with a MAP range of 27.5 mmHg in the normotensive group, 43.2 mmHg in the preeclampsia group, and 31.5 mmHg in the superimposed preeclampsia group, but only the difference between the preeclampsia and normotensive groups reached statistical significance (p= 0.02).Significance. Static autoregulation curves generated using third-order polynomials showed distinct characteristics in postpartum participants with normotension, preeclampsia, and superimposed preeclampsia, and suggested a wider cerebral autoregulatory range in those with preeclampsia.

Objective.Integration of cerebral blood macrocirculation and microcirculation form a crucial aspect of global cerebral blood flow. Our study aimed to investigate time delay between pulse oscillations of cerebral blood flow velocity (FV) and total hemoglobin concentration (tHb) acquired via transcranial Doppler ultrasonography and functional near-infrared spectroscopy, respectively. Additionally, we compared time-related characteristics with cerebral arterial time constant (τ).Approach.The study involved monitoring of FV, tHb, arterial blood pressure and end-tidal CO₂(EtCO₂) during 5 min of rest (normocapnia) and 5 min of controlled hypercapnia in 36 healthy subjects (age: 24 years,Q1-Q3: 21-27 years, 15 females). Onset time delay (onset TD) was defined as the time offset between onsets of FV and tHb pulses, and time to tHb max (TTM) as a time difference between the FV pulse onset and tHb pulse maximum.τwas calculated as a product of cerebrovascular compliance and resistance. Onset TD and TTM were compared between normocapnia and hypercapnia, and their associations withτwere assessed.Main results.Onset TD was consistently positive (0.075 s,Q1-Q3: 0.065-0.108 s). Onset TD andτwere significantly shorter in hypercapnia (0.075 s vs 0.071 s,p< 0.001; 0.107 s vs 0.099 s,p< 0.001). Changes in onset TD andτbetween normo- and hypercapnia were significantly correlated (R_S= 0.452,p< 0.01). TTM was correlated withτin normocapnia (R_S= 0.364,p= 0.03), hypercapnia (R_S= 0.407,p= 0.01) and in terms of relative changes (R_S= 0.421,p= 0.01).Significance.There is a time delay between cerebral macro- and microcirculation, which becomes shorter as the cerebral vasculature dilates in hypercapnia.

Objective.To investigate the association of phase angle (PhA) with body fat percentage, lean soft tissue, and bone mineral density (BMD) in adolescent athletes overall and stratified by sex and sexual maturity stage.Approach.A cross-sectional study was conducted with 112 adolescent athletes (67 boys, 14.25 ± 2.08 years) who practiced indoor volleyball, beach volleyball, swimming, Track and Field, or basketball. BMD, lean soft tissue, and body fat percentage were estimated using dual-energy x-ray absorptiometry. PhA was determined using bioelectrical impedance analysis (50 kHz). Associations between PhA and body composition were tested using multiple linear regression in three models: (1) total sample, (2) stratified by sex, and (3) stratified by maturation. Covariates generally included skin color/race, sport modality, time in the sport, and weekly training volume.Main results.In the overall sample, PhA showed a negative association with body fat percentage and a positive association with lean soft tissue and BMD. When the results were stratified by sex, there was a negative association with fat percentage in girls and positive associations with lean soft tissue and BMD in boys. Analysis by sexual maturity stage revealed that PhA was negatively associated with body fat percentage in pubertal athletes and positively associated with lean soft tissue and BMD in both pubertal and post-pubertal athletes.Significance.PhA was positively associated with lean soft tissue and BMD and negatively associated with fat percentage. However, associations varied by sex and sexual maturity, underscoring the importance of accounting for these biological variables when assessing relationships between body composition and PhA in athletes.

Objective.The aortic pressure waveform (APW) is relevant to diagnosing and treating cardiovascular diseases. While various non-invasive methods for APW estimation exist, more accurate and practical monitoring methods are required. This study introduces a hybrid model combining variational mode decomposition improved by particle swarm optimization (PSO-VMD) and gated recurrent unit (GRU) networks (PSO-VMD-GRU) to reconstruct the APW from the brachial pressure waveform (BPW).Approach.The model was verified using invasive APWs and BPWs. Data synthesis generated additional samples. The synthetic BPWs were decomposed into multiple intrinsic mode functions (IMFs) using PSO-VMD. A GRU was trained to map the relationship between the IMFs and synthetic APWs. The proposed model was evaluated by comparing the mean absolute errors and Spearman's correlation coefficients (SCCs) of reconstructed total waveform (TW) and key hemodynamic indices including systolic, diastolic and pulse pressures (SP, DP and PP, respectively) against those from generalized transfer function (GTF) and other neural network-based methods, including temporal convolutional network (TCN), and bi-directional long short-term memory and self-attention mechanism (CBi-SAN).Main results.Among the four methods, PSO-VMD-GRU achieved the highest SCCs for TW (0.9912) and DP (0.9676), while TCN performed the best for SP (0.9850) and PP (0.9875). In MAE comparisons, PSO-VMD-GRU matched CBi-SAN across TW, SP, DP, and PP, while surpassing GTF in TW (2.44 versus 2.66 mmHg) and DP (1.61 versus 1.94 mmHg), and outperforming TCN in DP (1.61 versus 1.93 mmHg).Significance.Experiment results have shown that integrating PSO-VMD with GRU improves the accuracy of APW reconstruction effectively.

Objective. The advancement of contactless methods of measuring the respiratory rate (RR) using RGB cameras demonstrates a significant potential for improving patient care in various environments. As these methods offer reliable and discreet monitoring, they can prevent severe health complications and improve outcomes for patients facing challenges accessing traditional healthcare facilities.Approach. This systematic review explores recent advancements in RR estimation using RGB cameras, focusing on assessing publicly available datasets and effective signal preprocessing methods. We also conducted a comprehensive analysis by comparing RGB camera-based approaches with other sensor modalities and discussed potential future research directions and indicated the necessity of developing new approaches that would mitigate existing challenges and would enhance the accuracy and reliability of non-contact RR measurement methods.Main results. We analyzed existing public datasets, assessing their diversity in lighting, skin tone, and motion, alongside the camera hardware configurations, including frame rate and resolution, utilizing different filter and feature-based techniques. While deep learning and hybrid models achieved lower errors under ideal indoor lighting and minimal motion, performance significantly declined in low light, high motion, or complex uncontrolled environments. In contrast, other sensor modalities, such as thermal and infrared sensors, achieved high accuracy across a wide range of conditions, but at greater hardware cost and system complexity, while RGB cameras remained the most cost-effective option, trading off precision for accessibility.Significance. RGB camera-based RR monitoring systems have the potential for robust applicability in clinical and nonclinical settings such as telemedicine platforms for monitoring patients breathing rates (BRs) in real time. This review highlights existing research gaps, such as insufficient real-world datasets and sensitivity to environmental variance, and emphasizes on the importance of acquiring datasets based on complex real-world scenarios, standardized benchmarks, multi-sensor fusion for addressing current limitations, and deep neural network architecture implementation for reliable non-contact RR estimation for real-world applications.