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: 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 GREIT reconstruction algorithm affects EIT 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 ARDS 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. 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: 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 bioimpedance spectroscopy (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 of in vitro cervical 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 of in vitro cervical samples. Particularly, this method was highly accurate in classifying CIN II p16(+)/CIN III, which has a higher risk of progression to cancer.

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 resistance R ̅. Three parameters are assessed in the calf and thigh of ten subjects by comparing <σ> with the local muscle thickness t by ultrasound, <Φ> with the local fat infiltration ratio FIR by ultrasound, and R ̅ with the ratio of extracellular water to total body water (ECW/TBW) e by bioelectrical impedance analysis (BIA). 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 using σ and Φ distributions. Moreover, <σ> has a positive correlation with t (correlation coefficient cc = 0.735, p < 0.05), <Φ> has a negative moderate correlation with the local FIR (cc = -0.585, p < 0.05), and R ̅ has a strong positive correlation with e (cc = 0.862, p < 0.001). Furthermore, multiple regression using <σ>, <Φ>, and R ̅ as explanatory variables has the highest correlation for ankle plantar flexion power (cc = 0.823, R^2 = 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, and R ̅ reflects extracellular water and local edema, which are collectively considered to constitute muscle strength.

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: 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 Squeeze-and-Excitation (SE) attention strengthens waveform feature representation and Bidirectional Long Short-Term Memory (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 (PCC) of 0.97 and a Signal-to-Noise Ratio (SNR) of approximately 13.64 dB. On the 12-lead dataset PaperECG_12L, 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.

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.