Physiological measurement最新文献

Objective.Remote photoplethysmography (rPPG) offers a promising method for contactless pulse rate (PR) monitoring, which is particularly valuable for remote patient care. However, signal noise-caused by factors such as motion and lighting-can significantly impact measurement accuracy.Approach.We present a hybrid algorithm that combines frequency-domain analysis to estimate initial PR and a time-domain approach to refine this estimate, improving robustness in challenging conditions.Main results.The combined method increases accuracy and success rate compared to time-domain methods alone. Evaluated against time-aligned electrocardiogram, it achieved a root mean square error (RMSE) as low as 2.0 bpm and anr²of 0.96. On a larger outpatient dataset, the RMSE was 3.2 bpm with anr²of 0.93. Importantly, no significant performance difference was observed across varying skin tones.Significance.These results demonstrate that the proposed PR algorithm enables reliable, contactless pulse monitoring in real-world conditions, supporting broader adoption of rPPG for inclusive and scalable remote health monitoring.

Objective.Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization, and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by predicting HF readmission.Methods.Seismocardiogram (SCG) signal is the low-frequency chest vibration produced by the mechanical activity of the heart. SCG signal was acquired from 101 patients with HF, including those readmitted to the hospital during the study period. SCG signals were segmented into heartbeats and clustered based on respiration phases. Features were extracted from each cluster. Several conventional machine learning (ML) models were developed using selected SCG and heart rate variability features. Furthermore, SCG signals were transformed into images using a time-frequency distribution method. Images were used to train a deep learning model. The models were able to predict the readmission status of HF patients.Results.ML algorithms achieved higher accuracy than the deep learning model in classifying the readmitted and non-readmitted HF patients. K-nearest neighbor achieved the highest classification accuracy (89.4% accuracy, 87.8% sensitivity, 90.1% specificity, 78.2% precision, and 82.7%F1-score). A detailed discussion of the extracted features was provided, correlating them with HF conditions.Conclusions. The study results suggest that SCG signals may be useful for readmission prediction of HF patients.

Objective.Exertional heat illness (EHI) remains a challenge for those that exercise in hot and humid environments. Physiological status monitoring is an attractive method for assessing EHI risk and a critical component of recommended layered risk management approaches. While there is consensus that some combination of core body temperature, mean skin temperature, heart rate, and hydration provide an indication of heat strain, a field-feasible metric that correlates to EHI incidence has not been identified.Approach.We present a comparison of five practicable heat strain indices (skin temperature, estimated core temperature, core-skin temperature difference, Physiological Strain Index (PSI), and Adaptive Physiological Strain Index (aPSI) for 5080 U.S. Marine Corps recruits during an intense multi-day physical assessment. We considered the ability of the calculated indices in predicting the 30 EHI cases that occurred during our study.Main results.aPSI and single-point skin temperature identified 86.7% and 83.3% of EHI cases, respectively (∼35 min alert time and ∼15% false positive rate). PSI and core-skin temperature difference were only able to identify 63.3% and 60% of EHI cases. Estimated core temperature only identified 23.3% of EHIs. Critically, the cases missed by aPSI included two individuals with fevers from viral infections, and two cases of heat exhaustion who had moderate field rectal temperatures (<39 °C); the rectal temperatures of false negatives forT_skranged from 38.3 °C-40.3 °C (mean 39.1 ± 0.7 °C).Significance.aPSI is demonstrated as the first field-practical exertional heat strain index that accurately identifies EHI risk in real time.

Objective.Electrocardiogram (ECG) analysis is vital for the diagnosis of cardiac conditions and monitoring human physiological states. However, challenges such as signal perturbations, inconsistent quality, and signal inference undermine the reliability of ECG analysis. Despite advances in large language models (LLMs), their application in enhancing ECG-based physiological measurements remains underexplored. To address these challenges, the objective is to develop a novel multimodal framework that integrates ECG signals with textual instructions for robust denoising and signal quality assessment, enabling effective physiological analysis across diverse tasks.Approach.We propose cross-modal attention (CMA)-ECG, a multimodal framework that employs a hybrid cross-attention mechanism to align ECG and text features with task-specific heads, combined with a pre-trained LLM for contextual enhancement. The framework leverages pretrained LLMs with 7B parameters, balancing accuracy and computational requirements for practical deployment.Main results.Extensive experiments on multiple datasets demonstrate that CMA-ECG achieves state-of-the-art (SOTA) performance in robustness to perturbations, quality assessment, and denoising. CMA-ECG achieves up to 8.8% higher area under the ROC curve in quality assessment and 20% lower mean squared error in denoising compared to SOTA baselines, ensuring reliable ECG processing.Significance.This approach advances ECG analysis by integrating signal and contextual data, offering a robust solution for physiological monitoring and analysis, ensuring reliable ECG processing.

Objective. This exploratory study investigates cyclical changes in physiological features across the menstrual cycle in women with epilepsy, focusing on their potential relationship with seizure occurrence.Approach. Nocturnal data during sleep were collected from two women with ovulatory cycles and compared with data from healthy controls, two non-ovulatory women, one postmenopausal woman, and two male patients. The aim was to characterize signal patterns across different reproductive states and to explore whether menstrual-related rhythms correspond to seizure timing. Circular statistics mapped signals onto an angular scale, allowing identification of biphasic patterns linked to ovulation, while machine learning algorithms identified ovulatory phases.Main Results. In ovulatory participants, seizure activity predominantly occurred around the late luteal and early follicular phases (p < 0.05), and non-uniform and biphaisc trends were observed in temperature, resembling patterns in healthy participants. In contrast, individuals taking enzyme-inducing antiepileptic drugs showed disrupted physiological rhythms. Although hormonal fluctuations appear to drive cyclical patterns, additional rhythms (e.g. weekly) were also observed, suggesting multifactorial influences.Significance. These preliminary findings underscore the need to account for menstrual and other biological cycles in seizure forecasting models and provide a foundation for future studies involving larger cohorts.

Objective.Sodium (Na) overconsumption has been associated with hypertension risk and progression. Human soft tissue and bone are recognized as quickly and slowly exchangeable compartments for sodium storage. How such a distribution regulates blood pressure remains unknown. This study performedin vivoNa measurements on human subjects who underwent dietary intervention, utilizing a compact neutron generator-based neutron activation analysis system. It aimed to evaluate the performance of this innovative system for body Na assessment.Approach. Participants were provided with low and high sodium diets. Baseline measurements were taken before each intervention feeding period, and follow-up measurements were conducted afterwards. The human hands were irradiated for 20 min, followed by 2 cycles of Na gamma ray collection. A biokinetic model was used to calculate sodium concentrations in soft tissue and bone, reflecting sodium accumulation in the two compartments.Main results. For soft tissue, Na levels after low Na diet decreased from baseline in half of the subjects, with reductions ranging from 8% to 55%. The other half of participants exhibited relatively stable Na content. Among participants consuming high Na diet, all participants had elevated Na in soft tissue compared to those on low Na diet. By contrast, Na in bone showed no significant changes from baseline and follow-up for either dietary intervention. Bone Na concentrations ranged from approximately 1000-2000 ppm.Significance. For the first time, Na in soft tissue and bone was measured in humans using neutron activation analysis in response to dietary interventions. This study demonstrates thatin vivoneutron activation analysis can be used to measure Na concentration in both soft tissue and bone. It successfully detects Na alteration in soft tissue and explores the biokinetics of Na retention following dietary interventions. Measuring soft tissue and bone sodium content is a potentially useful approach to study diet and disease links affected by sodium.

Objective.Accurate and non-invasive estimation of core body temperature (CBT) is essential for preventing heat-related illnesses during physical activity and thermal stress. The objective of this work is to develop and evaluate a framework for real-time CBT estimation using only heart rate (HR) data, enabling a lightweight solution suitable for deployment on wearable devices.Approach.We propose a multi-model Kalman filtering (KF) framework with variance-based fusion. Two variants were developed: a supervised Physiological State-Specific KF (PSSK) that uses activity labels (rest, exercise, recovery) to train distinct models, and an unsupervised trial clustering-based KF (TCBK) that clusters trials based on HR-CBT features to capture latent physiological variability without state annotations. Both models were evaluated on two independent datasets and compared against baseline methods.Main results.In within-dataset evaluations, TCBK achieved the highest accuracy with a root mean square error (RMSE) of 0.38∘C (Dataset 1) and 0.41∘C (Dataset 2). In cross-dataset generalization, PSSK demonstrated superior robustness with an RMSE of 0.88∘C, whereas the TCBK model's error increased to 1.56∘C. Both proposed models outperformed the established Buller and Falcone models.Significance.This work demonstrates that lightweight, HR-only models can provide accurate CBT estimation by incorporating state- or context-aware modeling. The framework offers a practical and deployable solution for continuous thermal strain monitoring in occupational and athletic settings, providing a balance between performance and real-world applicability for wearable technology.

Introduction.Neurovascular coupling (NVC) represents multiple mechanisms that adapt cerebral blood flow to neural activity. This study hypothesized that two NVC paradigms active hand movement (AHM) and active elbow flexion (AEF) would elicit similar hemodynamic responses.Methods.Seventeen healthy subjects (9 females, mean age: 34 ± 3 years) performed both motor paradigms. Each session began with a 1.5 min rest (baseline), followed by 1 min of motor paradigm (T1), and a 1.5 min recovery (T2). Transcranial Doppler was used to monitor cerebral blood velocity (CBv) in middle cerebral artery. Arterial blood pressure (ABP), heart rate (HR), and end-tidal CO₂(ETCO₂) were continuously monitored. Data were analyzed using two-way repeated measures ANOVA (p< 0.05).Results.Both AEF and AHM elicited significant increases in CBv over time (p< 0.05), with similar temporal profiles between paradigms. For AEF, CBv in the dominant hemisphere increased from 100% ± 1 at baseline to 104% ± 4 at T1 (p< 0.05) and returned to 98% ± 4 at T2. Similarly, AHM increased CBv from 100% ± 1 at baseline to 105% ± 6 at T1 (p< 0.05) and 98% ± 4 at T2. Significant reductions in cerebrovascular resistance and critical closing pressure were observed at T1 compared to baseline, followed by an increase at T2 (p< 0.05). HR showed significant changes, while resistance area product, ABP, and ETCO₂remained stable throughout the experiment.Conclusion.AHM produced hemodynamic responses comparable to AEF, with an increase in CBv through vasodilation via non-myogenic responses. In this study we demonstrated that the maneuver is a valid alternative to AEF in NVC studies.

Objective.Wrist-worn photoplethysmography (PPG) enables scalable, long-term unobtrusive sleep monitoring through the expression of sympathetic and parasympathetic activity in heart rate variability. However, interindividual differences in the sympatho-vagal balance may inherently limited general PPG-based sleep staging models. This study investigates whether learning individual autonomic representations through model personalization can improve PPG-based automated sleep staging performance.Approach.Concurrent wrist-worn PPG and wearable electroencephalography (EEG) were collected during home monitoring for up to seven nights in a heterogeneous sleep-disordered population (n= 59). Personalization was performed through finetuning (i.e. partial retraining) a general PPG-based model by coupling the subject-specific PPG data with the wearable EEG stage classifications. Performance of the general and personalized models were compared on PPG acquired during a gold-standard clinical polysomnography, testing their agreement on 4-stage classification (W/N1+N2/N3/REM) with the manual scoring.Main result.Overall performance increased in 82.5% of the subjects, with significantly improved performance reached when personalizing the model on three or more training nights. Performance increased with personalization on additional training nights for each stage: wake (β= .005,p< .001), N1+N2 (β= .003,p< .001), N3 (β= .004,p< .001), and REM (β= .005,p< .001). Effects were strongest for younger individuals (β= .009,p< .001) and patients with insomnia (β= .011,p< .001). Personalization greatly impacted the derived sleep macrostructural sleep parameters, with considerable improvement in N3 sleep classification, and in capturing rapid eye movement (REM) sleep fragmentation.Significance.Personalization can overcome one-size-fits-all limitations of a general model and should be considered for PPG-based sleep staging when an altered autonomic modulation is expected that deviates from the general model's global representation.