Physiological measurement最新文献

Objective: Instantaneous determination of left ventricular (LV) diastolic function would be a useful aid in diagnosis and treatment of heart failure. The time constant of LV pressure decay (also known as Tau) is an established metric for evaluating LV stiffness and assessing LV diastolic function. Approach: In this study, we present a novel approach that uses a single arterial (aortic) pressure waveform to classify abnormal Tau through a physics-based machine learning (ML) methodology. This study is based on a clinical LV catheterization at the University of Southern California Keck Medical Center. We included 54 patients (13 females, age 36-90 (66.3±10.8), BMI 20.2-38.5 (27.8±4.6)) that were subjected to the same exclusion criteria of the primary study. Invasive pressure waveforms at LV and ascending aorta were measured using 2.5 F transducer tipped electronic micro-catheters. Intrinsic frequency (IF) parameters were computed from aortic pressure waveforms. Tau was calculated using an exponential curve-fitting approach based on LV pressure. Tau ranges were 25.7-86.5 ms (50.3±11), and Tau = 48 ms was used as a binary classification cut-off. Random forest and K-nearest neighbors classifiers were trained on 44 patients and blindly tested on 10 patients. 3- fold cross-validation was used to prevent overfitting. Main Results: Our proposed ML classifier model accurately predicts true Tau classes using physics-based features, where the most accurate one demonstrates 80.0% (elevated) and 100.0% (normal) success in predicting true Tau classes on blind data. Significance: We demonstrate that our proposed physics-based ML models can instantaneously classify Tau using information from a single aortic pressure waveform. Although an invasive proof, the required model inputs can be acquired non-invasively using carotid waveforms, working toward a smartphone-based, patient-activated tool for assessing diastolic dysfunction. .

Objective.The left ventricle (LV) pressure-volume (PV) loop provides comprehensive characteristic information into ventricular mechanics, aiding in the assessment of systolic and diastolic function. However, its routine clinical application is limited due to the invasiveness of conventional LV catheterization procedures. This study introduces a novel analytical framework for estimating LV pressure (LVP) waveforms noninvasively, using carotid pressure waveforms and routine cardiac imaging.Approach.The proposed method consists of a five-step analytical approach that integrates physical and physiological LV-aortic coupling relationships with a novel ventricular filling model. To assess the sensitivity and effectiveness of our method, we applied it on a clinical sample of 77 people (42% female), comprising healthy volunteers and heart failure (HF) patients, and analyzed the reconstructed PV-loops for key hemodynamic metrics.Main results.The proposed method robustly captured key hemodynamic changes associated with HF patients, including elevated LV end-diastolic pressure (p< 0.01), loss of inotropy (p< 0.001), and impaired ventricular efficiency (p< 0.001). Additionally, HF patients exhibited significantly smaller stroke work (p< 0.001), mean external power (p< 0.01), and contractility (p< 0.001) compared to the control group. These results align well with established clinical observations for HF, demonstrating the method's ability to detect pathological ventricular modifications.Significance.The proposed noninvasive LVP estimation method provides physiologically and clinically relevant PV-loop metrics without requiring invasive catheterization. By reliably capturing ventricular dysfunction in HF patients, this approach offers a promising alternative for noninvasive cardiac assessment. Its ability to enable routine evaluation of LV mechanics has the potential to improve HF diagnosis and therapeutic management, facilitating earlier intervention and more personalized treatment strategies.

Objective.The potential of optical pumped magnetometer magnetocardiography (OPM-MCG) for diagnosing coronary artery disease (CAD) has been initially shown, yet lacks large-scale prospective research.Approach.Using invasive coronary angiography (ICA) as a reference, we constructed three feature sets for the development of machine learning (ML) models: a 'Heart' feature set consisting only of OPM-MCG features, a 'Clinical' feature set, and a 'Heart + Clinical' combined feature set. We assessed the performance of 11 ML models with 10-fold cross-validation and conducted a feature importance analysis.Main results and Significance. Among 1513 participants (mean age 58.2 ± 12.0 years, 75.5% male), 1194 (78.92%) tested positive for ICA. Significant differences were observed in 'Heart' and 'Clinical' features between ICA-positive and negative groups. ML models using only 'Heart' features (AUC 0.84-0.88) outperformed those using only 'Clinical' features (AUC 0.62-0.75). Combining both feature types improved diagnostic accuracy (AUC 0.75-0.90). Feature importance analysis highlighted that 'Significant change of Ar-PN' in OPM-MCG was key for ICA diagnosis (47.8%), along with 'Abnormal Sp-TT', 'Significant change of Ps-PN', and 'Abnormal Mg-TT'. OPM-MCG has high performance in diagnosing CAD, and the significant change of Ar-PN is the most important feature. Cat Boost and random forest are more suitable for OPM-MCG to build ML diagnostic models for CAD.

Objectives.This study aimed to develop and validate a load cell-based device for measuring isometric forearm rotation torque and to determine its test-retest reliability.Approach.The custom-built device was calibrated using known weights and validated against a high-precision torque transducer. For reliability assessment, 35 physically active participants (20 males, 15 females; age 30 ± 7 years) were tested for isometric forearm pronation and supination strength 5-7 d apart.Main results.The custom device demonstrated excellent validity (intraclass correlation coefficient (ICC), absolute agreement = 1.00;r²= 1.00,p< 0.001; mean difference = -1.26-1.44%,p< 0.001). Test-retest reliability was excellent for absolute pronation and supination torque (ICC = 0.88-0.97; coefficient of variation percentage (CV%) = 4.1-5.6; minimal detectable change (MDC) at 90% confidence level = 13.1-19.9%), good to excellent for supination:pronation ratios (ICC = 0.60-0.88; CV% = 7.0-8.6; MDC = 0.10-0.13), and fair to good for dominant:non-dominant ratios (ICC = 0.42-0.66; CV% = 6.1-7.6; MDC = 0.07-0.10). Sex significantly influenced absolute torque values, with males demonstrating consistently higher torque, although reliability metrics were similar for both sexes.Significance.The device is valid, and the test is reliable. It is suitable for clinical assessments, rehabilitation monitoring, and performance evaluation, facilitating an improved understanding of factors affecting elbow overloading and injuries. Limb ratio metrics should be interpreted with caution due to their lower reliability.

Objective.Various time domain features, including dicrotic notch (dic), in photoplethysmogram (PPG), and the pulse transit time (PTT) determined using the simultaneously recorded electrocardiogram (ECG), are believed to have a critical role with many potential clinical applications. However, the dependence of these parameters on PPG sensor location is less well known.Approach.Three transmissive pulse oximetry probes (Xhale) were put simultaneously on the ear, nose, and finger of 36 healthy volunteers in the lower body negative pressure (LBNP) experiment. Various features of the recorded PPG signals were analyzed across different LBNP phases for each location. Simultaneously recorded finger PPG and ECG (Nellcor) were used to assess the dependence of PTT on PPG sensor location.Main results.PPG signal quality varies by measurement site, with nasal PPG showing the highest quality and ear PPG the lowest. Except pulse rate (PR), most feature-related indices differ across sites. Specifically, the ratios of detectabledicvary, highest in finger PPG and lowest in nasal PPG. Whendicis detectable, theepoint anddicare significantly different. PR variability indices and PTT also vary by location, though no clear conclusions can be drawn about PTT behavior across different LBNP phases.Significance.Various indices derived from PPG signals in a well-controlled study environment are influenced by sensor placement. Although not all possible indices are examined, the findings clearly illustrate the sensitivity of signal features to measurement location. While these results may not be directly generalizable to routine clinical settings, caution is warranted when extrapolating findings from one PPG site to another. This consideration is especially important in the digital health era, where mobile devices with PPG sensors are increasingly deployed at diverse body sites.

Objective: Depression is a prevalent mental health disorder that significantly impacts well-being and quality of life. This study investigates the relationship between depression and cardiovascular function, exploring time-series features derived from electrocardiogram (ECG) and photoplethysmogram (PPG) data as potential biomarkers for depression prescreening. Approach: As part of a comprehensive psycholinguistic experiment, we collected data from 60 individuals, including both healthy participants and those with varying levels of depression, assessed using the Beck Depression Inventory-II (BDI-II) and the Patient Health Questionnaire-9 (PHQ-9). Bimodal features derived from both ECG and PPG data were used to develop machine learning models for depression risk classification, employing classifiers such as Random Forest, XGBoost, Logistic Regression, and Support Vector Machines (SVM). Additionally, regression models were built to predict depression severity based on ECG- and PPG-derived biomarkers. Main Results: Key findings indicate that short-term variability (SD1) features in the ECG RR interval, peripheral systolic and diastolic phases from the PPG, and pulse duration significantly differ between healthy individuals and those at risk of depression. SVM achieved the best classification performance, with an AUROC of 0.83 ± 0.11 for BDI-II-based classification and 0.78 ± 0.11 for PHQ-9-based classification. SHAP analysis consistently identified systolic-SD1 and RR-SD1 as key predictors. Regression analysis further supported the role of cardiovascular features in assessing depression severity, yielding a mean absolute error (MAE) of 10.18 for BDI-II and 5.27 for PHQ-9 score regression. Significance: This study demonstrates the feasibility of using wearable ECG and PPG technologies for depression prescreening. The findings suggest that cardiac activity-based biomarkers can contribute to the development of cost-effective, objective, and non-invasive tools for mental health assessment, complementing traditional diagnostic methods.

Objectives.To quantify and evaluate the dynamic response of RR intervals (RRI) and heart rate (HR) measurements of commercially available Bluetooth chest-worn HR monitors during induced rapid changes in HR.Approach.An arbitrary function generator created synthetic electrocardiogram signals simulating the heart activity. Different scenarios of rapid changes in HR were simulated several times using: (1) step responses; (2) exercise data (EX); and (3) intermittent EX data. RRI and HR were recorded using the standard Bluetooth HR service for four wearable monitors: Garmin HRM-Dual, Movesense active, Polar H10, and Wahoo TRACKR. RRI latency, HR latency, and agreement were evaluated from the reference signal.Main results.RRI latency (median and interquartile range) was 0.7(0.5,0.7) s for Garmin, 0.4(0.2,0.5) s for Movesense, 2.6(2.2,2.8) s for Polar, and 2.1(1.9,2.4) s for Wahoo, where results did not differ greatly between tests. HR response latency was different between devices and tests. During intermittent EX tests, HR latency was 3.3(3.0, 3.3) s for Garmin, 1.0(1.0,1.0) s for Movesense, 2.3(2.3,2.3) s for Polar, and 2.2(2.2,2.3) s for Wahoo, where all devices consistently underestimated HR peaks and overestimated HR valleys, with a greater discrepancy in HR valleys.Significance.Most validation protocols of RRI and HR measured by wearable monitors neglect their dynamic characteristics. The present study demonstrated that manufacturers implemented different digital filters to compute the HR values, limiting the devices' ability to capture rapid HR changes. Open documentation of the processing steps is advised, and use cases involving sharp HR changes-such as intermittent high-intensity training-should rely on beat-to-beat RRI recordings.

Physics-informed neural networks (PINNs) represent a transformative approach to data models by incorporating known physical laws into neural network training, thereby improving model generalizability, reduce data dependency, and enhance interpretability. Like many other fields in engineering and science, the analysis of physiological signals has been influenced by PINNs in recent years. This manuscript provides a comprehensive overview of PINNs from various perspectives in the physiological signal analysis domain. After exploring the literature and screening the search results, more than 40 key studies in the related domain are selected and categorized based on both practically and theoretically significant perspectives, including input data types, applications, physics-informed models, and neural network architectures. While the advantages of PINNs in tackling forward and inverse problems in physiological signal contexts are highlighted, challenges such as noisy inputs, computational complexity, loss function types, and overall model configuration are discussed, providing insights into future research directions and improvements. This work can serve as a guiding resource for researchers exploring PINNs in biomedical and physiological signal processing, paving the way for more precise, data-efficient, and clinically relevant solutions.

Objective.The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical evolution of septic patients and to evaluate the predictive ability of features.Approach. Data from intensive care unit septic patients were extracted from the freely available HiRID database and a comprehensive pipeline for time series analysis of critical care data was developed. Predictive models of cardiovascular deterioration (based on mean pressure and lactate values) and global organ dysfunction (based on SOFA score) were developed, and the addition of variability, such as entropies, cross-entropies and cross-correlation of heart rate and blood pressure (BP), was tested against the use of standard metrics alone.Main results.The best model achieved an area under the ROC curve value of 0.9671, with SOFA score values and trends being the most important features in the model, followed by features related to lactate, fluid balance, therapy and entropy values of BP.Significance.The results show that the dynamics of vital signs and their cross-coupling, as captured by the proposed variability indices, can provide additional insights into the physiological responses to the therapy administered.

Objective.This study evaluates the potential of photoplethysmography imaging (PPGI) with automated facial tracking for detecting hemodynamic and autonomic changes induced by lower-body negative pressure (LBNP). The goal is to assess whether PPGI-derived facial perfusion variations are related with stroke volume (SV), systemic vascular resistance (SVR), heart rate variability (HRV), and autonomic responses to progressive hypovolemia.Approach.Twenty-four healthy adults (8 females, 16 males; aged 28.7 ± 3.5 years) underwent a seven-stage LBNP protocol (-15 to -60 mmHg, recovery). Facial perfusion was recorded using cross-polarized PPGI, along with SV, SVR, HR, and mean arterial pressure. Facial landmark tracking (MediaPipe) was used to extract region-specific PPGI signals. Wavelet synchrosqueezing transform enabled spectral analysis, and HRV was assessed with NeuroKit2.Main Results.At -60 mmHg, the LBNP-intolerant group showed a 25.2% decrease in SV (p< 0.0001) and a 19% increase in SVR (p= 0.041). At -30 mmHg recovery, SV remained reduced by 21% (p< 0.001), with SVR elevated by 30.1% (p= 0.002). In contrast, the tolerant group exhibited SV increases of 12% and 18% at these stages (bothp< 0.0001), and a HR reduction of up to 5% (p< 0.05), with a decreasing SVR trend. HRV analysis indicated greater sympathetic activation in the intolerant group, with reduced HF power (p= 0.037) and increased LF/HF ratio (3.5 at -60 mmHg,p= 0.020). First harmonic PPGI amplitudes significantly declined in the intolerant group, most notably in the cheeks (-44.2%,p= 0.005).Significance.These findings suggests that PPGI, combined with AI-based face tracking and wavelet analysis, enables non-invasive, spatially resolved monitoring of vascular and autonomic responses. PPGI differentiates tolerant and intolerant groups, supporting its potential for real-time cardiovascular assessment in critical care and emergency settings.