Physiological measurement最新文献

Objective.Sleep is hypothesized to restore near-critical dynamics in large-scale brain networks, whereas insomnia may disrupt this self-organizing process. This study aimed to determine whether insomnia alters neural avalanche dynamics and criticality-based EEG metrics, and whether these metrics enhance prediction of sleep fragmentation compared with conventional spectral measures.Approach.Overnight high-density electroencephalography was recorded from 50 participants aged 16-69 years, including healthy sleepers and individuals with insomnia. Neural avalanches were detected as clusters of significant amplitude excursions. The branching parameter (σ) quantified temporal propagation within avalanches, while the deviation-from-criticality coefficient (DCC) indexed the system's distance from the critical state. These criticality features were contrasted with spectral power measures in predictive models of non-rapid eye movement (NREM) sleep fragmentation.Main results.Participants with insomnia exhibited reduced avalanche density and diminished slow-wave activity, accompanied by significant deviations ofσfrom the critical value and elevated DCC across the night. Criticality-based metrics captured fragmentation dynamics more sensitively than spectral features. In predictive modeling, criticality measures significantly outperformed spectral power in forecasting NREM fragmentation (F1-score = 0.69 vs. 0.62), with the strongest gains in mild and severe insomnia subgroups.Significance.Insomnia is characterized by a persistent deviation from near-critical neural dynamics, reflecting compromised stability and recovery during sleep. Criticality-based EEG features provide a more mechanistic and predictive framework for identifying sleep fragmentation and may offer novel biomarkers for quantifying disrupted sleep physiology in clinical insomnia.

Objective. Pendelluft is the movement of air between lung regions, which results in an apparent phase-shift of the aeration curve between lung regions. Electrical impedance tomography (EIT) can be used to detect and quantify pendelluft at the bedside. A common method to select the functional lung space (FLS)-i.e. the pixels associated with ventilation-is applying a threshold to the pixel tidal impedance variation (TIV). Due to the apparent phase shift, pixel TIV is lower in regions associated with pendelluft, resulting in removal of those pixels for further analysis.Approach. We developed a novel method for FLS selection using the established watershed segmentation method. Watershed regions are segmented based on the pixel amplitude map and local peaks in that map. Watershed regions whose local peaks fall inside the threshold-based TIV FLS are included. Pixels with an amplitude above the threshold that are inside the included watershed regions form the Watershed FLS.Main results. We evaluated the algorithm in 11 patients switching from controlled mechanical ventilation (CMV) to assisted mechanical ventilation (AMV). No significant differences were found between TIV FLS and Watershed FLS during CMV. Switching from CMV to AMV lead to a significant decrease of the TIV FLS (p= 0.043), but not the Watershed FLS. As a result, TIV FLS was significantly smaller than Watershed FLS during AMV (p⩽ 0.001). Pendelluft magnitude was higher using the Watershed FLS compared to the TIV FLS during AMV (p⩽ 0.001).Significance. The common TIV-based FLS can result in the unintended removal of pendelluft-associated pixels for further analysis. The Watershed FLS includes these pixels, potentially improving the quality of EIT-analysis in patients with spontaneous breathing efforts.

Objective. This paper describes a new method for measuring human bone density based on the measurement of bone electrical conductivity (BEC) using the well-known technique: electrical impedance tomography (EIT).Approach. The hypothesis is that BEC is directly related to bone mineral density. The proposed method consists of measuring the EIT, and then obtaining the conductivity values of the bones, and take this value to associate this one to a level of porosity. A model of skin, muscle, and bone was developed using computer simulation to vary the porosity in bones and obtain an inverse model in the form of an equation to measure three levels of porosity.Main results. The behavior of different porosity levels was simulated, and impedance tomography was applied; it is shown that electrical conductivity varies according to bone porosity. A relationship was subsequently obtained between the measurement of conductivity and bone density, creating a new non-invasive method for possible application to early detection of osteoporosis.Significance. This is a novel, non-invasive method for possible application on the early detection of osteoporosis at three different levels of bone porosity.

Objective.Calf blood pressure (CBP) plays an important role in various clinical applications, such as determining the appropriate cuff pressure for compression therapy and diagnosis of lower extremity vascular diseases, which necessitates the integration of built-in measurement methods within the device. This study aimed to investigate the differences in human CBP assessments resulting from the application of variousin vivoexternal compression strategies.Approach.An experimental procedure incorporating different compression strategies, specially the low-pressure-sustained mode, the slow-inflation (SI) mode and the slow-deflation (SD) mode, was conducted to capture the dynamic responses of the photoplethysmography (PPG) signal for CBP evaluation. Nineteen subjects, including 13 males and 6 females, participated in this experimental study. Feature points related to CBP were extracted from dynamic responses of the PPG signal and subjected to statistical analysis. A lumped parameter model of the human lower extremity was developed to assist in analyzing the biomechanical factors underlying these differences.Main results.The experimental results indicated that the dynamic behaviors of the PPG signal followed clear and consistent patterns, and the CBP value derived from the PPG signal in the SI mode was significantly higher than that obtained in the SD mode. Model simulation results showed that the differences in the evaluated values of the CBP between the SI and SD modes were caused by the different collapse processes of calf arteries and veins.Significance.This study could help understand the forced collapse process of blood vessels in the calf and inspire new ideas on CBP evaluation and personalized compression therapy.

Objective.Wearable devices with embedded photoplethysmography (PPG) enable continuous non-invasive monitoring of cardiac activity, offering a promising strategy to reduce the global burden of cardiovascular diseases. However, monitoring during daily life introduces motion artifacts that can compromise the signals. Traditional signal decomposition techniques often fail with severe artifacts. Deep learning denoisers are more effective but have poorer interpretability, which is critical for clinical acceptance. This study proposes a framework that combines the advantages of both signal decomposition and deep learning approaches.Approach.We leverage algorithm unfolding to integrate prior knowledge about the PPG structure into a deep neural network, improving its interpretability. A learned convolutional sparse coding model encodes the signal into a sparse representation using a learned dictionary of kernels that capture recurrent morphological patterns. The network is trained for denoising using the PulseDB dataset and a synthetic motion artifact model from the literature. Performance is benchmarked with PPG during daily activities using the PPG-DaLiA dataset and compared with two reference deep learning methods.Main results.On the synthetic dataset, the proposed method, on average, improved the signal-to-noise ratio (SNR) from -7.06 dB to 11.23 dB and reduced the heart rate mean absolute error (MAE) by 55%. On the PPG-DaLiA dataset, the MAE decreased by 23%. The proposed method obtained higher SNR and comparable MAE to the reference methods.Significance.Our method effectively enhances the quality of PPG signals from wearable devices and enables the extraction of meaningful waveform features, which may inspire innovative tools for monitoring cardiovascular diseases.

Objective.Arrhythmia classification from electrocardiograms (ECGs) suffers from high false positive rates and limited cross-dataset generalization, particularly for atrial fibrillation (AF) detection where specificity ranges from 0.72 to 0.98 using conventional 30 s analysis windows. While conventional deep learning approaches analyze isolated 30 s ECG windows, many arrhythmias, particularly AF and atrial flutter, exhibit diagnostic features that emerge over extended time scales.Approach.We introduce S4ECG, a deep learning architecture based on structured state-space models (S4), designed to capture long-range temporal dependencies by jointly analyzing multiple consecutive ECG windows spanning up to 2  min. We evaluated S4ECG on four publicly available databases for multi-class arrhythmia classification, including systematic cross-dataset evaluations to assess out-of-distribution robustness.Main results.Multi-window analysis consistently outperformed single-window approaches across all datasets, improving the macro-averaged area under the receiver operating characteristic curve by 1.0-11.6 percentage points. For AF detection specifically, specificity increased from 0.718-0.979 (single-window) to 0.967-0.998 (multi-window) at a fixed sensitivity threshold, representing a 3-10 fold reduction in false positive rates.Significance.Comparative analysis against convolutional neural network baselines demonstrated superior performance of the S4 architecture. Cross-dataset evaluation revealed that multi-window approaches substantially improved generalization performance, with smaller performance degradation when models were tested on held-out datasets from different institutions and acquisition protocols. A systematic investigation revealed optimal diagnostic windows of 10-20 min, beyond which performance plateaus or degrades. These findings demonstrate that structured incorporation of extended temporal context enhances both arrhythmia classification accuracy and cross-dataset robustness. The identified optimal temporal windows provide practical guidance for ECG monitoring system design and may reflect underlying physiological timescales of arrhythmogenic dynamics.

Objective.Infrared thermography is projected as an innovative and very promising tool for the observation of muscle response to fatigue. The aim of this study was to observe the skin temperature (Tsk) by thermography about acute muscle fatigue and delayed soreness (DOMS) following an exercise protocol until exhaustion in the triceps suralis.Approach.An open longitudinal descriptive observational study of the posterior leg region was performed in 73 healthy subjects. Data on age, sex, body mass index and triceps suralis thermography pre, post and 24 h after maximum muscle fatigue physical exercise, as well as pressure-pain threshold (PPT) and pain sensation by analogic visual scale (VAS) were collected.Main Results.Results showed significant difference in skin temperature over time (Tsk B: 30.1 °C (CI 95% (29.7-30.3), Tsk POST: 29.9 °C (CI 95% (29.6-30.2) and Tsk 24 H: 30.6 °C (CI 95% (30.3-30.9),p= <0.001;η2p= 0.272), side (Tsk right: 30.2 °C (CI 95% (29.4-30.3) and Tsk left: 30.1 °C (CI 95% (29.8-30.4),p= 0.021;η2p= 0.072) and a time x side interaction (Right Tsk B: 30.1 °C (CI 95% (29.8-30.4), Tsk POST: 29.9 °C (CI 95% (29.6-30.2), Tsk 24 H: 30.6 °C (CI 95% (30.3-30.9) and Left Tsk B: 30.0 °C (CI 95% (29.6-30.3), Tsk POST: 29.8 °C (CI 95% (29.5-30.1), Tsk 24 H: 30.6 °C (CI 95% (30.3-30.9),p= 0.011;η2p= 0.061). Regarding the PPT, significant changes were observed over time (B: 9.11 Kg (CI 95% (8.3-10.1), POST: 10.5 Kg (CI 95% (9.7-11.6) and 24 H: 7.64 Kg (CI 95% (7.0-8.3),p= <0.001;η²p= 0.328) and in the interaction between time and sex (men B: 11.0 Kg (CI 95% (9.7-12.3), POST: 12.5 Kg (CI 95% (11.1-13.9), 24 H: 8.8 Kg (CI 95% (7.8-9.7) and women B: 7.4 kg (CI 95% (6.1-8.7), POST: 8.8 Kg (CI 95% (7.4-10.1), 24 H = 6.6 Kg (CI 95% (5.7-7.5),p= 0.050;η²p= 0.041). Finally, the VAS scores showed significant changes over time (B: 0.32 cm (CI 95% (0.18-0.43) and 24 H: 4.46 cm (CI 95% (3.85-5.17),p= <0.001;η²p= 0.831).Significance.According to the results obtained, this technique could be a reliable method to evaluate DOMS. Exploring the integration of thermography with other modalities could provide a global understanding of muscle recovery processes.

Excessive daytime sleepiness (EDS) refers to a physiological state where individuals have difficulty remaining alert during the day. Managing EDS is particularly challenging to study and treat due to its multifaceted nature. Assessment methods include both subjective and objective approaches. Subjective evaluation often relies on simple, widely accepted, and widely used questionnaires; however, these tools are inherently limited by self-reporting bias. Objective assessment, on the other hand, primarily involves two well-known and reliable tests, but these are costly, time-consuming, and impractical for use outside of sleep units. Therefore, developing an objective tool that can quickly and accurately detect a decline in alertness, while remaining reliable, easy to use, and affordable, is of critical importance for sleep clinicians, safety organizations, and researchers. According to PRISMA guidelines, we did a systematic analysis of 95 studies that used photoplethysmography (PPG) for assessing EDS, drowsiness, and/or fatigue during the last 15 years (2010-2025). With advances in wearable technology, particularly through PPG and artificial intelligence, achieving this goal may be attainable. The next essential step is rigorous validation against established gold-standard tests to ensure the tool meets scientific and clinical standards for widespread adoption.

Objective.As cardiovascular diseases continue to rise, the accurate and convenient calculation of stroke volume (SV) and cardiac output (CO) has become an important topic. Studies have shown that electrical impedance tomography (EIT) can provide continuous non-invasive SV measurements. Despite its potential, a review of the various calculation methods for EIT-based SV and CO, along with their clinical utility, is lacking.Approach. A literature search was conducted on PubMed and Web of Science Core Collection. Full-text research articles in English were reviewed and discussed.Main results. In recent years, advancements in technology, clinical research, and intelligent algorithms have revealed EIT's substantial potential in SV monitoring.Significance. This article offers a review of the evolution of EIT technology in measuring SV, introducing various calculation methods, their advantages, challenges, and clinical applications.

Objective.Autonomous sensory meridian response (ASMR) is a tingling sensation induced while attending to specific sounds, including whispering, tapping, scratching, or other soft, repetitive noises. While previous studies focused on low arousal-positive emotions such as relaxation and calmness, this study explores a broader range of emotions elicited by ASMR auditory stimuli, including happiness, sadness, and disgust.Approach.The proposed study collects the multi-modal physiological data from electroencephalography, photoplethysmography, and electrodermal activity via wearable bio-sensors from 23 ASMR-experiencing participants while exposed to different ASMR-inducing auditory stimuli. It employs the rmANOVA test on the collected physiological responses and self-reported ratings for quantitative analysis and results in a significant difference between the emotions induced from the four audio stimuli, i.e. Happy from A1, Sad from A2, Calm from A3, Disgust from A4, and the neutral state. The proposed study also applies deep learning classifiers, artificial neural network (ANN), and convolution neural network (CNN) to the collected multi-modal physiological data to classify the four induced emotions from the ASMR auditory stimuli using the dimensions of arousal, valence, and dominance.Main results. The classification accuracy results from ANN, and CNN prove an excellent success rate of 96.12% and 74.25% with multi-modal valence-arousal-dominance for ANN and CNN, respectively, in classifying the four emotions induced by ASMR stimuli. And the statistical rmANOVA test results indicated distinctions among the four emotions, as thep-values exceeded the significance threshold of 0.05.Significance.The results highlight the effectiveness of multi-modal physiological signals and deep learning in reliably classifying ASMR-induced emotions, contributing to advancements in emotion recognition for mental health and therapeutic applications.