Physiological measurement最新文献

Scope. This focus collection aims at presenting recent advances in electrical impedance tomography (EIT), including algorithms, hardware, and clinical applications.Editorial. This focus collection of articles published by the journalPhysiological Measurementintroduces the Progress in EIT and Bioimpedance. It follows conferences in South Korea and Germany, that provided a platform for new research ideas.

Objective.Instantaneous, non-invasive evaluation of left ventricular end-diastolic pressure (LVEDP) would have significant value in the diagnosis and treatment of heart failure. A new approach called cardiac triangle mapping (CTM) has been recently proposed, which can provide a non-invasive estimate of LVEDP. We hypothesized that a hybrid machine-learning (ML) method based on CTM can instantaneously identify an elevated LVEDP using simultaneously measured femoral pressure waveform and electrocardiogram (ECG).Approach.We studied 46 patients (Age: 39-90 (66.4 ± 9.9), BMI: 20.2-36.8 (27.6 ± 4.1), 12 females) scheduled for clinical left heart catheterizations or coronary angiograms at University of Southern California Keck Medical Center. Exclusion criteria included severe mitral/aortic valve disease; severe carotid stenosis; aortic abnormalities; ventricular paced rhythm; left bundle branch and anterior fascicular blocks; interventricular conduction delay; and atrial fibrillation. Invasive LVEDP and pressure waveforms at the iliac bifurcation were measured using transducer-tipped Millar catheters with simultaneous ECG. LVEDP range was 9.3-40.5 mmHg. LVEDP = 18 mmHg was used as cutoff. Random forest (RF) classifiers were trained using data from 36 patients and blindly tested on 10 patients.Main results.Our proposed ML classifier models accurately predict true LVEDP classes using appropriate physics-based features, where the most accurate demonstrates 100.0% (elevated) and 80.0% (normal) success in predicting true LVEDP classes on blind data.Significance.We demonstrated that physics-based ML models can instantaneously classify LVEDP using information from femoral waveforms and ECGs. Although an invasive validation, the required ML inputs can be potentially obtained non-invasively.

Objective.The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist.Approach.We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set.Main results.All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92.Significance.We compared performance of four models on an open-access dataset.

Objective. Physiological data are often low quality and thereby compromises the effectiveness of related health monitoring. The primary goal of this study is to develop a robust foundation model that can effectively handle low-quality issue in physiological data.Approach. We introduce SiamQuality, a self-supervised learning approach using convolutional neural networks (CNNs) as the backbone. SiamQuality learns to generate similar representations for both high and low quality photoplethysmography (PPG) signals that originate from similar physiological states. We leveraged a substantial dataset of PPG signals from hospitalized intensive care patients, comprised of over 36 million 30 s PPG pairs.Main results. After pre-training the SiamQuality model, it was fine-tuned and tested on six PPG downstream tasks focusing on cardiovascular monitoring. Notably, in tasks such as respiratory rate estimation and atrial fibrillation detection, the model's performance exceeded the state-of-the-art by 75% and 5%, respectively. The results highlight the effectiveness of our model across all evaluated tasks, demonstrating significant improvements, especially in applications for heart monitoring on wearable devices.Significance. This study underscores the potential of CNNs as a robust backbone for foundation models tailored to physiological data, emphasizing their capability to maintain performance despite variations in data quality. The success of the SiamQuality model in handling real-world, variable-quality data opens new avenues for the development of more reliable and efficient healthcare monitoring technologies.

Objective.Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency (TF) analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs).Approach.ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the TF representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and Montreal Archive of Sleep Studies (MASS) benchmark databases. We also quantify spindle IF dynamics.Main results.ConceFT-S achieves F1 scores of 0.765 in Dream and 0.791 in MASS, which surpass A7 and SUMO. We reveal that spindle IF is generally nonlinear.Significance.ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.

Objective.In patients with suspected thoracic outlet syndrome (TOS), diagnosing inter-scalene compression could lead to minimally invasive treatments. During photo-plethysmography, completing a 30 s 90° abduction, external rotation ('surrender' position) by addition of a 15 s 90° antepulsion 'prayer' position, allows quantitative bilateral analysis of both arterial (A-PPG) and venous (V-PPG) results. We aimed at determining the proportion of isolated arterial compression with photo-plethysmography in TOS-suspected patients.Approach.We studied 116 subjects recruited over 4 months (43.3 ± 11.8 years old, 69% females). Fingertip A-PPG and forearm V-PPG were recorded on both sides at 125 Hz and 4 Hz respectively. A-PPG was converted to PPG amplitude and expressed as percentage of resting amplitude (% rest). V-PPG was expressed as percentage of the maximal value (% max) observed during the 'Surrender-Prayer' maneuver. Impairment of arterial inflow during the surrender (As+) or prayer (Ap+) phases were defined as a pulse-amplitude either <5% rest, or <25% rest. Incomplete venous emptying during the surrender (Vs+) or prayer (Vp+) phases were defined as V-PPG values either <70% max, or <87% max.Main results.Of the 16 possible associations of encodings, As - Vs - Ap - Vp- was the most frequent observation assumed to be a normal response. Isolated arterial inflow without venous outflow (As + Vs-) impairment in the surrender position was observed in 10.3% (95%CI: 6.7%-15.0%) to 15.1% (95%CI: 10.7%-20.4%) of limbs.Significance.Simultaneous A-PPG and V-PPG can discriminate arterial from venous compression and then potentially inter-scalene from other levels of compressions. As such, it opens new perspectives in evaluation and treatment of TOS.

Objective.We extract walking features from raw accelerometry data while accounting for varying cadence and commonality of features among subjects. Walking is the most performed type of physical activity. Thus, we explore if an individual's physical health is related to these walking features.Approach.We use data collected using ActiGraph GT3X+ devices (sampling rate = 80 Hz) as part of the developmental epidemiologic cohort study,I= 48, age =78.7±5.7years, 45.8% women. We apply structured functional principal component analysis (SFPCA) to extract features from walking signals on both, the subject-specific and the subject-spectrum-specific level of a fast-paced 400 m walk, an indicator of aerobic fitness in older adults. We also use the subject-specific level feature scores to study their associations with age and physical performance measures. Specifically, we transform the raw data into the frequency domain by applying local Fast Fourier Transform to obtain the walking spectra. SFPCA decomposes these spectra into easily interpretable walking features expressed as cadence and acceleration, which can be related to physical performance.Main results.We found that five subject-specific and 19 subject-spectrum-specific level features explained more than 85% of their respective level variation, thus significantly reducing the complexity of the data. Our results show that 54% of the total data variation arises at the subject-specific and 46% at the subject-spectrum-specific level. Moreover, we found that higher acceleration magnitude at the cadence was associated with younger age, lower BMI, faster average cadence and higher short physical performance battery scores. Lower acceleration magnitude at the cadence and higher acceleration magnitude at cadence multiples 2.5 and 3.5 are related to older age and higher blood pressure.Significance.SFPCA extracted subject-specific level empirical walking features which were meaningfully associated with several health indicators and younger age. Thus, an individual's walking pattern could shed light on subclinical stages of somatic diseases.

Objective.Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation.Approach.This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer.Mainresults.The search yielded 1039 papers and the final analysis included 115 papers. A total of 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning (ML) approaches (22%), the use of existing cut-points (18%), receiver operating characteristic curves to determine cut-points (14%), and other strategies including regressions and non-ML algorithms (8%).Significance.ML techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.

Objectives. Phase angle muscle imaging has been proposed by phase angle electrical impedance tomography (ΦEIT) under electrical muscle stimulation (EMS) for long-term monitoring of muscle quality improvement, especially focusing on calf muscles.Approach. In the experiments, twenty-four subjects are randomly assigned either to three groups: control group (CG,n= 8), low voltage intensity of EMS training group (LG,n= 8), and optimal voltage intensity of EMS training group (OG,n= 8).Main results. From the experimental results, phase angle distribution imagesФare cleared reconstructed by ФEIT as four muscle compartments over five weeks experiments, which are called theM₁muscle compartments composed of gastrocnemius muscle,M₂muscle compartments composed of soleus muscle,M₃muscle compartments composed of tibialis-posterior muscle, flexor digitorum longus muscle, and flexor pollicis longus muscle, andM₄muscle compartment composed of the tibialis anterior muscle, extensor digitorum longus muscle, and peroneus longus muscle.Фis inversely correlated with age, namely theФdecreases with increasing age. A paired samplest-test was conducted to elucidate the statistical significance of spatial-mean phase angle in all domain <Ф>_Ωand in each muscle compartment <Ф>_Mwith reference to the conventional phase angle Ф by bioelectrical impedance analysis, muscle grey-scaleG_muscleby ultrasound, and maximal dynamic strengthS_Maxby one-repetition maximum test.Significance. From thet-test results, <Ф>_Ωhave good correlation with Ф andS_Max. In the OG, <Ф^W5>_Ω,Ф^W5, and (S_Max)^W5were significantly higher than in the first week (n= 8,p< 0.05). A significant increase in the phase angle of bothM₁andM₄muscle compartments is observed after five weeks in LG and OG groups. Only the OG group shows a significant increase in the phase angle ofM₂muscle compartment after five weeks. However, no significant changes in the spatial-mean phase angle ofM₃compartment are observed in each group. In conclusion, ФEIT satisfactorily monitors the response of each compartment in calf muscle to long-term EMS training.