Physiological measurement最新文献

Objective. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-driven algorithms for DR staging from digital fundus images (DFIs) often struggle with generalization due to distribution shifts between training and target domains.Approach. To address this, DRStageNet, a deep learning model, was developed using six public and independent datasets with 91 984 DFIs from diverse demographics. Five pretrained self-supervised vision transformers (ViTs) were benchmarked, with the best further trained using a multi-source domain (MSD) fine-tuning strategy.Main results. DINOv2 showed a 27.4% improvement in L-Kappa versus other pretrained ViT. MSD fine-tuning improved performance in four of five target domains. The error analysis revealing 60% of errors due to incorrect labels, 77.5% of which were correctly classified by DRStageNet.Significance. We developed DRStageNet, a DL model for DR, designed to accurately stage the condition while addressing the challenge of generalizing performance across target domains. The model and explainability heatmaps are available atwww.aimlab-technion.com/lirot-ai.

Objective: The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.

Approach: A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.

Main results: The heart rate of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error (MAE) of 1.31 BPM comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 BPM.

Significance: The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of heart rates (70-115 BPM), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Objective: Electrical Impedance Tomography (EIT) is a non-invasive technique used for lung imaging. A significant challenge in EIT is reconstructing images of deeper thoracic regions due to the low sensitivity of boundary voltages to internal conductivity variations. The current injection pattern is decisive as it influences the current path, boundary voltages, and their sensitivity to tissue changes. Approach. This study introduces a novel current injection pattern with radially placed electrodes excited in a rotating radial pattern. The effectiveness of the proposed pattern was investigated using a 3D computational model that mimics the human thorax, replicating its geometry and tissue electrical properties. To examine the detection of lung anomalies, models representing both healthy and unhealthy states, including cancer-like anomalies in three different positions, were developed. The new pattern was compared to common patterns-Adjacent, Skip 1, and Opposite-using Finite Element Analysis (FEA). The comparison focused on the current density within lung nodules and the sensitivity to changes in anomaly positions. Main Results. Results showed that the new pattern achieved the maximum current density within anomalies compared to surrounding tissues, with peak values near the closest electrode pairs to the anomalies. Specifically, current density magnitudes reached 72.73 10^{-9} A.m, 145.24 10^{-9} A.m, and 26.43 10^{-9} A.m for the three different positions, respectively. Furthermore, the novel pattern's sensitivity to anomaly position changes surpassed the common patterns.

Significance: These results demonstrate the efficiency of the proposed injection pattern in detecting lung anomalies compared to the common injection patterns.

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

Objective: The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.

Approach: A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.

Main results: The heart rate of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error (MAE) of 1.31 BPM comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 BPM.

Significance: The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of heart rates (70-115 BPM), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Objective.The surface electromyography (EMG) signal reflects the user's intended actions and has become the important signal source for human-computer interaction. However, classification models trained on EMG signals from the same day cannot be applied for different days due to the time-varying characteristics of the EMG signal and the influence of electrodes shift caused by device wearing for different days, which hinders the application of commercial prosthetics. This type of gesture recognition for different days is usually referred to as long-term gesture recognition.Approach.To address this issue, we propose a long-term gesture recognition method by optimizing feature extraction, dimensionality reduction, and classification model calibration in EMG signal recognition. Our method extracts differential common spatial patterns features and then conduct dimensionality reduction with non-negative matrix factorization, effectively reducing the influence of the non-stationarity of the EMG signals. Based on clustering and classification self-training scheme, we select samples with high confidence from unlabeled samples to adaptively updates the model before daily formal use.Main results.We verify the feasibility of our method on a dataset consisting of 30 d of gesture data. The proposed gesture recognition scheme achieves accuracy over 90%, similar to the performance of daily calibration with labeled data. However, our method needs only one repetition of unlabeled gestures samples to update the classification model before daily formal use.Significance.From the results we can conclude that the proposed method can not only ensure superior performance, but also greatly facilitate the daily use, which is especially suitable for long-term application.

Objective.To investigate how severe abdominal aortic calcification (SAAC) and estimated pulse wave velocity (ePWV) relate to each other and to all-cause and cardiovascular disease (CVD) mortalities.Approach.National Health and Nutrition Examination Survey 2013-2014 data were analyzed. ePWV, computed using age and mean blood pressure, served as an independent variable. Dependent variable SAAC (AAC score >6) was quantified using dual-energy x-ray absorptiometry and Kauppila grading. A weighted logistic regression model, interaction terms, and restricted cubic spline analysis examined relationship between ePWV and SAAC. Kaplan-Meier curves were drawn among SAAC people. A weighted Cox regression model was built to examine associations of ePWV with all-cause and CVD mortalities.Main results.2849 individuals were included. A strong positive connection (odds ratio (OR) > 1,P< 0.05) was seen between ePWV and SAAC risk. Interaction termP-value indicated that only ethnicity (P< 0.05) had an impact on this link but smoking, alcohol use, age, sex, body mass index, or hypertension did not. SAAC patients showed greater ePWV, all-cause and CVD mortalities (P< 0.05) than non-SAAC subjects. Greater ePWV (>12.00 m s^-1) was associated with higher risks of all-cause and CVD mortalities in SAAC participants (hazard ratio (HR) > 1,P< 0.05). Significance.This study, for the first time based on the NHANES database, reveals a positive correlation between ePWV and SAAC, and identifies ePWV as an independent predictor of all-cause and cardiovascular mortality risk in patients with SAAC, providing a new biomarker for the prevention and early intervention of cardiovascular diseases.

Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

Objective.Carotid artery stenosis (CAS) is a key factor in pathological conditions, such as thrombosis, which is closely linked to hemodynamic parameters. Existing research often focuses on analyzing the influence of geometric characteristics at the stenosis site, making it difficult to predict the effects of overall vascular geometry on hemodynamic parameters. The objective of this study is to comprehensively examine the influence of geometric morphology at different degrees of CAS and at bifurcation sites on hemodynamic parameters.Approach.A three-dimensional model is established using computed tomography angiography images, and eight geometric parameters of each patient are measured by MIMICS. Then, computational fluid dynamics is utilized to investigate 60 patients with varying degrees of stenosis (10%-95%). Time and grid tests are conducted to optimize settings, and results are validated through comparison with reference calculations. Subsequently, correlation analysis using SPSS is performed to examine the relationship between the eight geometric parameters and four hemodynamic parameters. In MATLAB, prediction models for the four hemodynamic parameters are developed using back propagation neural networks (BPNN) and multiple linear regression.Main results.The BPNN model significantly outperforms the multiple linear regression model, reducing mean absolute error, mean squared error, and root mean squared error by 91.7%, 93.9%, and 75.5%, respectively, and increasingR²from 19.0% to 88.0%. This greatly improves fitting accuracy and reduces errors. This study elucidates the correlation and patterns of geometric parameters of vascular stenosis and bifurcation in evaluating hemodynamic parameters of CAS.Significance.This study opens up new avenues for improving the diagnosis, treatment, and clinical management strategies of CAS.

Objective. Intracranial pressure measurement (ICP) is an essential component of deriving of multivariate data metrics foundational to improving understanding of high temporal relationships in cerebral physiology. A significant barrier to this work is artifact ridden data. As such, the objective of this review was to examine the existing literature pertinent to ICP artifact management.Methods.A search of five databases (BIOSIS, SCOPUS, EMBASE, PubMed, and Cochrane Library) was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines with the PRISMA Extension for Scoping Review. The search question examined the methods for artifact management for ICP signals measured in human/animals.Results.The search yielded 5875 unique results. There were 19 articles included in this review based on inclusion/exclusion criteria and article references. Each method presented was categorized as: (1) valid ICP pulse detection algorithms and (2) ICP artifact identification and removal methods. Machine learning-based and filter-based methods indicated the best results for artifact management; however, it was not possible to elucidate a single most robust method.Conclusion.There is a significant lack of standardization in the metrics of effectiveness in artifact removal which makes comparison difficult across studies. Differences in artifacts observed on patient neuropathological health and recording methodologies have not been thoroughly examined and introduce additional uncertainty regarding effectiveness.Significance. This work provides critical insights into existing literature pertaining to ICP artifact management as it highlights holes in the literature that need to be adequately addressed in the establishment of robust artifact management methodologies.