Physiological measurement最新文献

Objective.Monitoring of intracranial pressure (ICP) in a clinical environment is critically important to the stability of patients with various acute neurological illnesses and injury including ischemic stroke, hemorrhagic stroke, brain tumor, and traumatic brain injury. This is because changes in ICP can cause significant stress on the brain and surrounding tissue through complications such as cerebral ischemia or hemorrhage in the surrounding area. Most ICP measurement techniques are invasive, expensive, and have poor spatial resolution. There has been some preliminary evidence to suggest that regional oxygen saturation (rSO₂) measured non-invasively by near-infrared spectroscopy (NIRS) has a statistical link to invasively obtained ICP. Given the limited exploration of this potential link, this scoping review (ScR) aims to investigate the current body of literature exploring the association between cerebral NIRS measurements and ICP.Approach.A comprehensive investigation was conducted across six major databases, with accordance to the preferred reporting items for systematic reviews and meta-analyzes guidelines, in order to evaluate the primary question of: What is the relationship between NIRS-derived cerebral signals and ICP?.Main results. The search process identified 3791 distinct articles. After screening based on the predefined criteria, 10 studies were deemed eligible for inclusion. An additional two studies were identified by screening the citation lists of the included studies. Overall, the collection of articles selected for this systematic ScR indicates a potential positive correlation between some cerebral NIRS variables and ICP; however, significant discrepancies and significant limitations exist in the literature.Significance.This review identifies a significant knowledge gap in the current understanding of how non-invasive NIRS metrics relate to ICP and highlights the importance of conducting additional experimentation in the field.

Objective. Hemodynamic monitoring is essential in preclinical research. Currently available techniques are either invasive or complex to implement. Inductive plethysmography (IP) provides an alternative for estimating stroke volume and cardiac output, as the IP signal includes ventilatory and cardiogenic oscillations (COS). COS monitoring, also defined as thoracocardiography (TCG), has been validated in humans and large laboratory animals. A recent study demonstrated proof of concept in COS extraction from the TCG signal recorded during respiratory pauses in mechanically-ventilated laboratory rats using a high-resolution IP device. The present study aims to develop an ensemble averaging (EA) algorithm, triggered by the electrocardiogram (ECG)R-peak, to extract COS from TCG signals in rats and continuously estimate stroke volume and cardiac output.Approach. After an evaluation of the IP device using the EA technique on a mechanical test bench, the applicability of the EA technique was tested in anesthetized rats without ventilatory support during a pharmacological challenge. The ability of the algorithm to track stroke volume and cardiac output changes during the hemodynamic test was also evaluated.Main results. Metrological evaluation of the IP device using the EA technique demonstrated linearity across the physiological operating range and resolution sufficient to detect volume changes of less than 10% of typical physiological values. Although the assumptions underlying the use of EA cannot be fully satisfied for COS extraction-due to quasi-synchrony with the ECGR-peak and signal non-stationarities-the method enabled extraction of satisfactory average COS waveforms, from which the system reliably captured positive and negative inotropic effects consistent with reference measurements during the pharmacological protocol.Significance. The evaluated algorithm demonstrates advancement over previous studies by enabling hemodynamic monitoring under usage conditions. Further studies are needed to extend its applicability to complex and physiologically relevant scenarios, positioning this technology as a potential non-invasive tool for preclinical research.

Objective. sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent photoplethysmography (PPG)-based deep learning models perform well on local datasets but struggle with external generalization due to data drift.Approach. this study evaluates multi-source domain training for improving out-of-distribution generalization in four-class sleep staging (wake, light, deep, rapid eye movement) from raw PPG time-series. The trained deep learning model is denoted SleepPPG-Net2. Additionally, we examined the impact of demographic factors, ethnicity, and obstructive sleep apnea (OSA) on performance. SleepPPG-Net2 was benchmarked against two state-of-the-art models.Main results. SleepPPG-Net2 outperformed benchmark models, improving generalization performance (Cohen's kappa) by up to 21%. Performance disparities were observed in relation to age, sex, and OSA severity.Significance. SleepPPG-Net2 enhances PPG-based sleep staging and provides insights into demographic and clinical influences on model performance.

Objective. Pulse arrival time (PAT) is known to be correlated with blood pressure. Although PAT can be measured using electrocardiography (ECG), photoplethysmography (PPG), and other signals commonly available in clinical settings, recent literature has noted that devices recording these waveforms are often subject to many hardware-specific factors related to digital filtering, clock synchronization, temporal resolution, and latency. These factors can introduce relative timing errors between the ECG and PPG signals, resulting in a situation where traditional approaches for PAT measurement will not work as intended.Approach. In this work, we propose a methodology that accounts for these confounding factors and generates precise measurements of PAT using standard bedside monitoring equipment. This technique involves using heart rate variability to match heartbeats across waveforms and experimentally profiling the timing systems of bedside medical devices to correct various timing-related artifacts. To improve the precision of the resulting PAT measurements, we model temporal uncertainties stemming from the finite temporal resolution of the waveform samples.Main results. We apply this approach to a dataset comprising approximately 1.6 million hours of continuous ECG and PPG data from over 10 000 unique patients in a pediatric intensive care unit. After demonstrating that the observed timing artifacts are consistent across the entire dataset, we show that accounting for them results in more reasonable distributions of PAT measurements across age groups.Significance. It is our hope that this work will spur discussion around the standardization of PAT measurement using routinely collected signals in a clinical environment.

Objective: To develop a comprehensive physiological dataset for assessing internal and external stress and to propose robust automated stress recognition methods based on photoplethysmographic (PPG) signals. Approach. We established the Internal and External Stress Dataset (IESD), comprising PPG signals from 107 participants subjected to four distinct stress-inducing paradigms. Exploratory analyses revealed significant differences in heart rate variability (HRV) across these paradigms, underscoring the necessity for advanced methods capable of differentiating various stress types. To address this, we introduced a transfer learning-based inter-paradigm stress recognition model utilizing a Domain Adversarial Neural Network (DANN) combined with Maximum Mean Discrepancy (MMD) for robust feature extraction. Main results. Analysis identified significant differences between internal and external stress, as well as among different external paradigms. Our proposed model demonstrated superior accuracy in recognizing homologous stress compared to heterologous stress within the same target domain, achieving accuracies of 73.86% (TSST to ST) and 60.41% (TSST to VWT). Moreover, the deep feature extraction significantly improved recognition performance and robustness across both intra- and inter-paradigm contexts. Significance. This study provides a valuable dataset and advanced methodology to enhance automated stress detection capabilities, effectively differentiating internal and external stress. The application of deep learning significantly improves recognition accuracy, offering promising prospects for future research and practical applications in stress monitoring.

Objective.Peripheral edema is a common issue among elderly individuals with chronic conditions such as heart failure (HF). Continuous, non-invasive monitoring may enable earlier intervention, reduced hospital readmissions, and improved quality of life. This systematic review aims to evaluate the use of bioimpedance (BI) as a method for monitoring peripheral edema, with a particular focus on portable and wearable applications for remote health management.Approach.A systematic search was conducted across PubMed, IEEE Xplore, and Web of Science to identify studies utilizing BI for the detection or monitoring of lower limb edema with potential for portability or wearability.Main results.Fourteen studies met the inclusion criteria. Five studies focused on HF patients, while nine involved other populations, such as healthy individuals, patients with limb injuries, or those on hemodialysis. Ten studies featured or proposed portable BI devices, whereas four remained at the proof-of-concept stage without portable implementations. There was significant variability in device design, measurement protocols, and target populations. While existing results show promise, few studies evaluated systems in real-world or long-term monitoring scenarios.Significance.BI is a promising, non-invasive approach for the continuous monitoring of peripheral edema, particularly in remote and home-based settings. However, current research is limited by small sample sizes, lack of standardization, and minimal validation in diverse, real-world environments. Further development of wearable systems and robust clinical validation is essential to support broader clinical adoption.

Objective.Electrocardiography and blood pressure (BP) measurement are two widely used tools for diagnosis and monitoring cardiovascular diseases. While the electrocardiogram (ECG) and BP have been considered complementary modalities, there are also systematic relationships between them. Therefore, advancements in portable and wearable ECG devices, along with promising results in cuff-less BP measurement using a combination of ECG and other bio-signals have led researchers to hypothesize the possibility of estimating BP and classifying BP categories (e.g. normal vs. hypertensive) using only ECG. However, the literature is divided on this topic: some studies support this hypothesis, while others reject it.Approach.In this study, regression and classification machine learning (ML) models were developed to explore the feasibility of estimating BP and predicting BP categories (normal vs. hypertensive) from 30 s ECGs using an extensive dataset from AliveCor Inc. which includes 124 427 records from 7412 subjects. The ECG and BP recordings were asynchronous with variable counts and time lags. Therefore, a 3.5 min time window before and after each ECG recording was used to calculate the mean BP measurement. Sex-aware ML models were trained using a comprehensive feature vector comprising 280 features: 128 explainable ECG features developed by the research team and 150 ECG features extracted by the Black Swan team, one of the top-performing teams in the PhysioNet Challenge 2017. Additionally, the average time gap between each ECG and the corresponding BP measurement, along with the subject's age, were included as two supplementary features.Main results.Our best regression ML models achieved a mean absolute error of 12.59 mmHg for estimating systolic BP and 7.43 mmHg for diastolic BP, with correlation coefficients of 0.35 and 0.38 between the predicted and actual values, respectively. The best BP normal-hypertensive classification model achieved an area under the receiver operating characteristic curve of 0.655.Significance.Using a large dataset of ECG and BP recordings, this study found that ML models did not achieve acceptable performance in predicting BP values or classifying BP categories, indicating that BP cannot be reliably estimated from the ECG.

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.