Physiological measurement最新文献

Objective.Quantification of ventilation inhomogeneity using electrical impedance tomography (EIT) typically relies on accurate identification of breathing cycles, which is often unreliable in spontaneously breathing patients. The objective of this study was to develop a robust, breath-independent metric for characterizing temporal ventilation heterogeneity.Approach.We propose a pixel asynchrony value (PAV), a window-based temporal correlation measure that quantifies the asynchrony between local pixel waveforms and a global ventilation reference without requiring breath segmentation. A global summary index, the global asynchronous index (GAI), is derived from spatial PAV distributions. The method was evaluated using EIT recordings from 21 high dependency unit patients acquired before and after airway clearance therapy.Main results.GAI demonstrated a consistent and significant reduction following treatment (p = 0.0011), indicating improved temporal synchrony of regional ventilation. In contrast, conventional inhomogeneity indices, including the global inhomogeneity index and the standard deviation of regional ventilation delay, showed weaker or inconsistent changes. Robustness analysis further showed that GAI remains stable across a range of window lengths and is insensitive to the absence of explicit breath-cycle detection.Significance.The proposed PAV-based GAI provides a physiologically interpretable and robust measure of temporal ventilation heterogeneity that can be applied without breath segmentation, making it particularly suitable for spontaneously breathing patients and routine clinical monitoring.

Objective.Develop and evaluate whether a model trained to detect the physiological signature of hemorrhage in intensive care unit (ICU) patients generalizes to other cohorts.Approach.We collected cardiorespiratory monitoring data and packed red blood cell administration data from consecutive adult admissions in one development and three evaluation ICU cohorts. We defined hemorrhage as three or more transfusions within 24 h. We trained a penalized logistic regression model to predict hemorrhage within 8 h and externally evaluated the predictions.Main results.The evaluation ICU cohorts comprised more than 6M q15 min observations. The cross-validated area under the receiver operating characteristic (AUC) in the development cohort was 0.706 (141 event admissions, 95% confidence intervals (CIs): 0.656-0.757) and 0.712 (968 event admissions, 95% CI: 0.693-0.726) for 17 591 medical and surgical ICU patients in the combination of 3 evaluation cohorts. The calibration slope of the hemorrhage model was close to unity (1.041, 95%CI: 0.956-1.127). Predicted risk increased significantly in the 8 h preceding clinical recognition of bleeding. There was no evidence of model performance drifting over time. There was evidence for lower performance for patients over 75 (20% lower than patients 18-44), among patients at University of Pittsburgh (Pitt) (24% lower than MIMIC III), Black patients (11% lower than White patients), and females (12% lower than males). The shock index also had reduced performance at Pitt, for female patients, and for patients over 75, though not for Black patients. The hemorrhage score had a higher net benefit than the shock index.Significance.Patients in ICUs have an increased risk for bleeding due to their chronic and acute illness, and earlier bleeding identification leads to better outcomes. A risk model for hemorrhage based only on continuous cardiorespiratory data has clinically relevant predictive performance that generalizes across three cohorts with different monitoring devices and electronic health record systems.

Objective. To evaluate the effects of static magnetic field (SMF) on avian brain and muscle tissues using electrical bioimpedance (EBI) as a non-invasive monitoring method, assessing changes in tissue impedance and phase angle to understand cellular responses under a 200 mT SMF.Approach. A custom experimental setup with needle electrodes was used to acquire EBI data from avian brain and muscle tissues exposed to 200 mT SMF for 60 min. Impedance and phase angle measurements were analyzed to assess tissue-specific responses. Frequency analysis and Lissajous curves were employed to identify signal amplitude changes.Main results. Brain tissue exhibited a faster exponential impedance decay (τ= 13.3 min) compared to muscle tissue (τ= 29.5 min). Phase angle measurements indicated capacitive membrane dynamics. Frequency analysis revealed low-frequency metabolic disruptions and stable 51 Hz oscillations. Lissajous curves showed SMF-induced reductions in low-frequency signal amplitudes, more pronounced in brain tissue.Significance. EBI proved effective for real-time, non-invasive monitoring of SMF-induced changes in tissue electric properties, highlighting tissue-specific responses. These findings suggest EBI's potential as a diagnostic tool for studying SMF effects, with future research needed to explore varied tissues and SMF intensities to enhance clinical applications.

Objective. This driving simulator experiment was conducted to examine the effect of prolonged automation on fatigue during conditionally automated driving (CAD). Driver fatigue, which can be distinguished from drowsiness and already accounts for as many as 20% of all road traffic accidents, is likely to remain a pervasive problem during CAD, as reductions in attention and vigilance due to fatigue could imperil safe transitions of control between automated system and user. While considerable research exists concerning drivers' responses under states of drowsiness or distraction, fewer studies have investigated the effect of automation on fatigue.Approach. Drivers' self-reported fatigue and workload, physiological responses, and takeover performance were examined across three driving conditions: a baseline period of manual driving, an automated driving condition in which drivers interacted with theN-back task, and a 50 min automated drive with no secondary task.Main results. Findings show that fatigue was significantly higher following 50 min of automated driving than at baseline, or while participants performed a non-driving-related task. Strikingly, 80% of participants experienced signs of sleepiness, and almost half had to exercise effort to stay awake. Fatigue also resulted in decreases in heart rate (HR) and relative beta power derived from electroencephalography, and an increase in blink rate and HR variability.Significance. Overall, the findings advance knowledge in this area by supporting the idea of fatigue as failure to adequately self-regulate during automation. Several physiological measures have also been identified as possible markers of fatigue to inform emerging monitoring technology.

Objective.Bioimpedance spectroscopy (BIS) has emerged as a promising technique for screening cervical intraepithelial neoplasia (CIN) since the electrical properties vary with the pathological status of cervical tissues. In this study, we aimed to evaluate the ability of CIN screening using multiple features extracted from BIS measurements collected with a multi-electrode BIS probe.Approach.This study enrolled 161 patients with gynecological diseases, including 44 with and 117 without cervical dysplasia. Upon the histological diagnosis, the samples were classified as normal, CIN I, and CIN II with p16 positive (p16(+))/CIN III. Complex impedance spectra ofin vitrocervical conization tissues were measured using the BIS probe. A Cole-Cole plot was generated from each patient's data measured on the conized cervix, and various features were extracted. Receiver operating characteristic (ROC) curves were generated, and the area under each ROC curve (AUC) was calculated.Main results.As a result, fifteen features from Cole-Cole plots differed significantly (p<0.01) between normal cervices and CIN. The AUCs based on multiple features, as determined by multivariable logistic regression, were 0.93 for normal cervix vs CIN I, 0.99 for normal cervix vs CIN II p16(+)/CIN III, and 0.94 for normal cervix vs CIN. These AUCs were improved by 14.8%, 7.6%, and 8.0%, respectively, compared with the results based on features extracted from only the real part of the impedance spectra.Significance.In conclusion, CIN can be accurately diagnosed using multiple features extracted from the impedance spectrum ofin vitrocervical samples. Particularly, this method was highly accurate in classifying CIN II p16(+)/CIN III, which has a higher risk of progression to cancer.

Objective.Time-difference electrical impedance tomography (EIT) is gaining widespread use for bedside lung monitoring in intensive care patients suffering from lung-related diseases. It involves collecting voltage measurements from electrodes placed on the patient's thorax, which are then used to reconstruct impedance images. This study investigates how incorporating anatomical information from CT data into the widely used Graz consensus reconstruction algorithm affects EIT (GREIT) images and improves their interpretability.Approach.Based on clinically motivated lung state scenarios, we simulated EIT measurements to assess how the GREIT parameters influence the result of EIT image reconstruction, particularly with respect to noise performance and image accuracy. We introduce quality measures that allow us to perform a quantitative assessment of reconstruction quality. We incorporate the anatomical features of a patient from CT data by customizing the background conductivity and the distribution of GREIT training targets.Main results.Our analysis confirmed that unphysiological background conductivity assumptions can lead to misleading EIT images, whereas physiological values, although more accurate, come with higher noise sensitivity. By increasing the number of GREIT training targets inside the lung and adapting the respective weighting radius, we significantly improved the anatomical accuracy of the EIT images. When applied to clinical EIT data from a representative acute respiratory distress syndrome patient, these adjustments in the reconstruction setup substantially enhanced the interpretability of the resulting EIT images.Significance.Incorporating CT-based anatomical data in the GREIT reconstruction significantly enhances the clinical applicability of EIT in lung monitoring. The improved interpretability of EIT images facilitates better-informed clinical decisions and the individualized adjustment of ventilation strategies for critically ill patients.

Objective.To establish population-specific, age- and sex-stratified electrocardiographic (ECG) reference ranges for Chinese children and adolescents using a data-driven approach, addressing the limitations of conventional empirically defined age groupings.Approach.A total of 35 088 ECG recordings from individuals under 18 years of age without structural heart disease or ECG abnormalities were analyzed. An unsupervised machine-learning clustering algorithm was applied to identify natural developmental trajectories of 149 ECG parameters and derive data-driven age intervals. Sex-specific stratification was performed to account for physiological differences. To assess physiological validity, we evaluated the ability of the newly derived reference ranges to identify ECG deviations in children with echocardiographically confirmed ventricular septal defects (VSDs).Main Results.Four distinct age-dependent variation patterns were identified across the 149 ECG parameters, enabling precise determination of age-specific intervals. Sex-related differences were observed for most measurements. When applied to children with VSD, the data-driven reference intervals demonstrated higher sensitivity in detecting ECG deviations compared with previously published standards.Significance.This study introduces a machine-learning-based paradigm for defining pediatric ECG reference values. The resulting age- and sex-specific thresholds more accurately reflect physiological maturation and cardiac loading changes than traditional reference sets, offering improved clinical relevance for pediatric ECG interpretation.

Objectives. Local muscle quantity and quality in the human leg have been assessed simultaneously across multiple muscle compartments by electrical impedance tomography (EIT) imaging.Approach.Three EIT parameters are defined, which are (1) the spatial-mean conductivity<σ>(2) the spatial-mean phase angle<Φ>, and (3) the mean resistanceR¯. Three parameters are assessed in the calf and thigh of ten subjects by comparing<σ>with the local muscle thicknesstby ultrasound,<Φ>with the local fat infiltration ratioFIRby ultrasound, andR¯with the ratio of extracellular water (ECW) to total body watereby bioelectrical impedance analysis. Moreover, three parameters are compared with isometric strength tests, which assess knee extension power, knee flexion power, ankle plantar flexion power, and ankle flexion power.Main results.As the experimental results, the local muscle quantity and quality in the calf and thigh of ten subjects were imaged usingσandΦdistributions. Moreover,<σ>has a positive correlation witht(correlation coefficientcc= 0.735,p< 0.05),<Φ>has a negative moderate correlation with the localFIR(cc= -0.585,p< 0.05), andR¯has a strong positive correlation withe(cc= 0.862,p< 0.001). Furthermore, multiple regression using<σ>,<Φ>, andR¯as explanatory variables has the highest correlation for ankle plantar flexion power (cc= 0.823,R2= 0.706) among isometric strength tests, which is related to the tibialis anterior muscle.Significance. The reason for the observed associations with local muscle quantity, quality, and strength is that<σ>reflects muscle fiber density characterized by high tissue conductivity,<Φ>reflects muscle quality related to cell membrane capacitance, structural integrity, and fat infiltration, andR¯reflects ECW and local edema, which are collectively considered to constitute muscle strength.

Objective.Photoplethysmography, a non-invasive optical technique that measures changes in blood volume in the microvascular bed of tissue, offers a promising approach for monitoring physiological changes during sleep. This study evaluates differential photoplethysmography signal patterns that can distinguish between apneas vs hypopneas, which are key features of sleep-related breathing disorders.Approach.We analyzed data from 263 severe (apnea hypopnea index ⩾30) obstructive sleep apnea patients, using recordings from the Multi-Ethnic Study of Atherosclerosis. Over 57 000 respiratory events occurring during stage N2 sleep were included. A machine learning model was trained on 89 features derived from the photoplethysmography signal, using the pyPPG toolbox, to classify: apneas vs hypopneas in the supine and lateral sleep posture, and posture-specific differences for each respiratory event type.Main results.Results showed that photoplethysmography signal characteristics significantly differed between apneas vs hypopneas. The model achieved an area under the receiver operation characteristic curve of 0.80 in the lateral posture and 0.83 in the supine posture. However, classification performance was low when distinguishing between apneas and hypopneas in the lateral vs the supine position with an area under the receiver operation characteristic curve of 0.62 for apneas and 0.64 for hypopneas. The discriminative signal features were consistent across different periods of the night.Significance.These findings indicate that photoplethysmography can detect meaningful differences in sleep-related breathing events and support its potential as a foundation for wearable diagnostic and monitoring tools that are personalized, accessible, and cost-effective.

Objective: The digitization of paper electrocardiograms (ECGs) faces several challenges, including amplified errors during segmentation and signal extraction, severe noise interference, and poor generalization under complex conditions. To address these issues, we propose an end-to-end signal location prediction model (SLPM).Approach: SLPM employs a classification-regression joint learning framework to directly predict the presence and vertical coordinate of each signal point, achieving precise mapping from ECG images to time-series signals. A hierarchical squeeze-and-excitation bidirectional long short-term memory (SE-BiLSTM) feature enhancement mechanism is integrated, where SE attention strengthens waveform feature representation and BiLSTM captures lateral temporal dependencies, thereby improving the continuity and stability of signal prediction.Main Results: Experiments on the single-lead datasets PaperECG_Clean and PaperECG_Enhanced, derived from the PTB-XL dataset, demonstrate that SLPM achieves high-accuracy digitization performance even under distortion conditions, with a Pearson correlation coefficient of 0.97 and a signal-to-noise ratio (SNR) of approximately 13.64 dB. On the 12-lead dataset PaperECG_12 l, the model attains an SNR of 14.66 dB with only 0.31 million parameters. Significance: these results indicate that SLPM offers notable advantages in accuracy, efficiency, and generalization, representing a promising new approach for the high-fidelity digitization of paper ECGs.