Physiological measurement最新文献

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.

Objective.The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments.Approach.This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments.Main results.The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model.Significance.The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model gu

Objective.In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.Approach.This study analyses the potential ofMulti-dimensional Ensemble Empirical Mode Decompositionby application of theComplete Ensemble Empirical Mode Decomposition with Adaptive Noiseand a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.Main results.In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on theRoot Mean Square Errorbetween the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland-Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal.Significance.Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional detrending and denoising. For all performed analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related signal and the PPG signal.

Objective.Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF).Approach.Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson's correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF.Main Results.Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min^-1m^-2, with ranging from 2.6 to 4.77 l min^-1m^-2. The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57,p< 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p= 0.35) and 0.03 (p= 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p< 0.01).Significance.This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.

Objective.Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method.Approach.This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG.Main results.The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33^∘. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities.Significance.This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method's objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

Objective.To determine the optimal frequency and site of stimulation for transcutaneous vagus nerve stimulation (tVNS) to induce acute changes in the autonomic profile (heart rate (HR), heart rate variability (HRV)) in healthy subjects (HS) and patients with heart failure (HF).Approach.We designed three single-blind, randomized, cross-over studies: (1) to compare the acute effect of left tVNS at 25 Hz and 10 Hz (n= 29, age 60 ± 7 years), (2) to compare the acute effect of left and right tVNS at the best frequency identified in study 1 (n= 28 age 61 ± 7 years), and (3) to compare the acute effect of the identified optimal stimulation protocol with sham stimulation in HS and HF patients (n= 30, age 59 ± 5 years, andn= 32, age 63 ± 7 years, respectively).Main results.In study 1, left tragus stimulation at 25 Hz was more effective than stimulation at 10 Hz in decreasing HR (-1.0 ± 1.2 bpm,p< 0.001 and -0.5 ± 1.6 bpm, respectively) and inducing vagal effects (significant increase in RMSSD, and HF power). In study 2, the HR reduction was greater with left than right tragus stimulation (-0.9 ± 1.5 bpm,p< 0.01 and -0.3 ± 1.4 bpm, respectively). In study 3 in HS, left tVNS at 25 Hz significantly reduced HR, whereas sham stimulation did not (-1.1 ± 1.2 bpm,p< 0.01 and -0.2 ± 2.9 bpm, respectively). In HF patients, both active and sham stimulation produced negligible effects.Significance.Left tVNS at 25 Hz is effective in acute modulation of cardiovascular autonomic control (HR, HRV) in HS but not in HF patients (NCT05789147).

Objective.The cerebral vasculature is formed of an intricate network of blood vessels over many different length scales. Changes in their structure and connection are implicated in multiple cerebrovascular and neurological disorders. In this study, we present a novel approach to the quantitative analysis of the cerebral macrovasculature using computational and mathematical tools in a large dataset.Approach.We analysed a publicly available vessel dataset from a cohort of 56 (32/24F/M) healthy subjects. This dataset includes digital reconstructions of human brain macrovasculatures. We then propose a new mathematical model to compute blood flow dynamics and pressure distributions within these 56-representative cerebral macrovasculatures and quantify the results across this cohort.Main results.Statistical analysis showed that the steady state level of cerebrovascular resistance (CVR) gradually increases with age in both men and women. These age-related changes in CVR are in good agreement with previously reported values. All subjects were found to have only small phase angles (<6°) between blood pressure and blood flow at the cardiac frequency.Significance.These results showed that the dynamic component of blood flow adds very little phase shift at the cardiac frequency, which implies that the cerebral macrocirculation can be regarded as close to steady state in its behaviour, at least in healthy populations, irrespective of age or sex. This implies that the phase shift observed in measurements of blood flow in cerebral vessels is caused by behaviour further down the vascular bed. This behaviour is important for future statistical models of the dynamic maintenance of oxygen and nutrient supply to the brain.