Journal of Electrical Bioimpedance最新文献

Electrosurgical generators (ESG) are widely used in medical procedures to cut and coagulate tissue. Accurate control of the output power is crucial for surgical success, but can be challenging to achieve. In this paper, a novel expert knowledge-based peak current mode controller (EK-PCMC) is proposed to regulate the output power of an ESG. The EK-PCMC leverages expert knowledge to adapt to changes in tissue impedance during surgical procedures. We compared the performance of the EK-PCMC with the classical peak current mode controller (PCMC) and fuzzy PID controller. The results demonstrate that the EK-PCMC significantly outperformed the PCMC, reducing the integral square error (ISE) and integral absolute error (IAE) by a factor of 3.88 and 4.86, respectively. In addition, the EK-PCMC outperformed the fuzzy PID controller in terms of transient response and steady-state performance. Our study highlights the effectiveness of the proposed EK-PCMC in improving the regulation of the output power of an ESG and improving surgical outcomes.

The image reconstruction in electrical impedance tomography (EIT) has low accuracy due to the approximation error between the measured voltage change and the approximated voltage change, from which the object cannot be accurately reconstructed and quantitatively evaluated. A voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) is proposed to reconstruct the image with high accuracy. In the OO-SME model, a sensitivity matrix of the object-field is estimated, and the sensitivity matrix change from the background-field to the object-field is estimated to optimize the approximated voltage change, from which the approximation error is eliminated to improve the reconstruction accuracy. Against the existing linear and nonlinear models, the approximation error in the OO-SME model is eliminated, thus an image with higher accuracy is reconstructed. The simulation shows that the OO-SME model reconstructs a more accurate image than the existing models for quantitative evaluation. The relative accuracy (RA) of reconstructed conductivity is increased up to 83.98% on average. The experiment of lean meat mass evaluation shows that the RA of lean meat mass is increased from 7.70% with the linear model to 54.60% with the OO-SME model. It is concluded that the OO-SME model reconstructs a more accurate image to evaluate the object quantitatively than the existing models.

The aim of the proposed study is to reveal the correlations between the dynamics of Respiratory Rate (RR) and Heart Rate (HR) during intermittent physical work at maximum power on a cycle ergometer. The stage of investigating the General functional athlete readiness (GFAR) was conducted using the sports standard "R-Engine" and the cycle ergometer in 16 volunteers (10 men, 6 women) whose average age was 21±1.17 years. To determine the athletic potential of the volunteers in this study, we used our own Coefficient of Anaerobic Capacity (CANAC Q, beats). Continuous registration of the heart rate and respiratory rate of volunteers in the maximum power sports test was performed by the "RheoCardioMonitor" system with a module of the athlete functional readiness based on the method of Transthoracic electrical impedance rheography (TEIRG). The degree of correlation of functional indicators (M, HRM, GFAR) with CANAC Q in all experimental series of the study group as a whole (n=80) was at a very high level, which confirmed the effectiveness of using the Coefficient of Anaerobic Capacity (CANAC Q) in assessing the general functional athlete readiness of the volunteers. CANAC Q is measured in "beats" of the heart and is recorded very accurately using the method of transthoracic electrical impedance rheography (TEIRG). For this reason, as a promising sports PSM-system, CANAC Q can replace the methods for determining the functional athlete readiness by blood lactate concentration and maximum oxygen consumption.

This study evaluated the influence of novel beverage formulations on bioimpedance- and urine-based hydration markers. Thirty young healthy adults (n=16 females, n=14 males; age: 23.2±3.7 years; BMI: 24.3±3.3 kg/m²) participated in a randomized, double-blind, placebo-controlled, crossover study. Participants completed three conditions with baseline bioimpedance, urine, and body mass assessments, followed by ingestion of one liter of a test beverage over a 30-minute period. The three beverages were: active hydration formulation in still (AFstill) or sparkling (AFspark) water and a still water control. The active formulations were identical in concentrations of alpha-cyclodextrin and complexing agents. Following beverage ingestion, bioimpedance assessments were performed every 15 minutes for two hours, followed by final urinary and body mass assessments. The primary bioimpedance outcomes were phase angle at 50 kHz, resistance of the extra-cellular compartment (R₀), and resistance of the intracellular compartment (Ri). Data were analyzed using linear mixed effects models, Friedman tests, and Wilcoxon tests. Statistically significant changes in phase angle values were observed at 30 (p=0.004) and 45 minutes (p=0.024) following the initiation of beverage ingestion in the AFstill condition as compared to the reference model (i.e., control condition at baseline). Although differences between conditions were not statistically significant at later time points, the data were consistent with AFstill having greater elevations in phase angle throughout the monitoring period. At the 30-minute time point only, statistically significant differences in R₀ for AFspark (p<0.001) and in Ri for AFstill (p=0.008) were observed. When averaged across post-ingestion time points, there was a trend (p=0.08) for Ri differences between conditions. The net fluid balance was greater than zero, indicating retention of ingested fluid, for AFstill (p=0.02) and control (p=0.03), with a trend for AFspark (p=0.06). In conclusion, an active formulation containing alpha-cyclodextrin in still water demonstrated potential benefits for enhancing hydration markers in humans.

Conductivity change in skin layers has been classified by source indicator o^k (k=1: Stratum corneum, k=2: Epidermis, k=3: Dermis, k=4: Fat, and k=5: Stratum corneum + Epidermis) trained from feedforward neural network (FNN) in bioelectrical impedance spectroscopy (BIS). In BIS studies, treating the skin as a bulk, limits the differentiation of conductivity changes in individual skin layers, however skin layer classification using FNN shows promise in accurately categorizing skin layers, which is essential for predicting source indicators o^k and initiating skin dielectric characteristics diagnosis. The o^k is trained by three main conceptual points which are (i) implementing FNN for predicting k in conductivity change, (ii) profiling four impedance inputs α_ξ consisting of magnitude input α|_z|, phase angle input α_θ, resistance input α_R, and reactance input α_x for filtering nonessential input, and (iii) selecting low and high frequency pair $\left(f_r^{lh}\right)$ by distribution of relaxation time (DRT) for eliminating parasitic noise effect. The training data set of FNN is generated to obtain the α_ξ ∈ R^10×17×10 by 10,200 cases by simulation under configuration and measurement parameters. The trained skin layer classification is validated through experiments with porcine skin under various sodium chloride (NaCl) solutions C_NaCl = {15, 20, 25, 30, 35}[mM] in the dermis layer. FNN successfully classified conductivity change in the dermis layer from experiment with accuracy of 90.6% for the bipolar set-up at $f_6^{lh}=10\&100[\text{kHz]}$ and with the same accuracy for the tetrapolar at $f_8^{lh}=35\&100[\text{kHz]}$. The measurement noise and systematic error in the experimental results are minimized by the proposed method using the feature extraction based on α_ξ at $f_r^{lh}$.

At present, there are no hardware or biochemical systems that allow to assess the severity of post-COVID syndrome in vivo. The hardware of the proposed biotechnical system is based on routine transthoracic electrical impedance rheography, which makes it possible to register the frequency characteristics of the patient's bioimpedance response to controlled stress stimulation, thereby simultaneously fixing the characteristics of his productive heart, the state of the hemomicrocirculatory bed, the efficiency of the gas transport function of his blood, and also reliably assess personal reactivity and adaptive potential. Subsequent mathematical approximation of the obtained biometric data by an original neural network makes it possible to rank the results obtained and automatically generate a program of medical rehabilitation for a particular patient, depending on the severity of his post-COVID syndrome. The study results proved two reliable physiological signs confirming the presence of latent post-COVID complications: a decrease in the base impedance value for light exercise and an increase in the length of the systolic arc of the rheocardiogram.

Background: Bioimpedance analysis (BIA) is a readily available tool to assess body composition in a clinical setting. BIA has received little attention in orthopaedics and namely joint arthroplasty. This study aims to quantify changes in body composition in the immediate peri-operative period following total joint arthroplasty.

Methods: Adults scheduled for elective total joint arthroplasty were recruited to participate. Patients underwent BIA scans in the immediate peri-operative period: pre-operative on their day of surgery, post-operative day 0, and post-operative day 1.

Results: 67 patients were enrolled to undergo BIA scans. Mean age was 62.64 ± 10.28 years old, and 49.2% were females. The all-supine cohort exhibited a 0.36 ± 0.61 kg increase in dry lean mass (p < 0.001) and 1.30 ± 2.14 kg increase in lean body mass on postoperative day 0 (p < 0.001). Patients received to 1.16 ± 0.58 kg of fluid mass, on average.

Conclusion: BIA is a rapid, portable tool that allows for body composition analysis of an inpatient surgical population. This study demonstrated that BIA can detect net fluid changes and may approximate implant mass following total joint arthroplasty. This may aid surgeons in interpreting post-operative body composition changes.

This study aimed to evaluate the changes in impedance and estimates of body composition variables obtained from segmental multi-frequency bioelectrical impedance analysis (SMFBIA) following acute hydration change. All participants (N = 11 active adults) had SMFBIA measurements at baseline (euhydration), post-dehydration, and post-hyperhydration in an experimental repeated-measures design. Dehydration and hyperhydration trials were randomized with the opposite treatment given 24 h later. Dehydration was achieved via a heat chamber of 40 °C and 60% relative humidity. Hyperhydration was achieved by drinking lightly-salted water (30 mmol·L^-1 NaCl; 1.76 g NaCl·L^-1) within 30 min. Post-measurements were taken 30 min after each treatment. Despite changes in mass post-dehydration (Δ = -2.0%, p < 0.001) and post-hyperhydration (Δ = 1.2%, p < 0.001), SMFBIA estimates of total body water (TBW) did not change significantly across trials (p = 0.507), leading to significant differences (p < 0.001) in SMFBIA-estimates of body fat percentage across trials. Dehydration resulted in a significant (p < 0.001) 8% decrease in limb impedances at both 20 kHz and 100 kHz. Hyperhydration increased limb impedances only slightly (1.5%, p > 0.05). Impedance changes in the trunk followed an opposite pattern of the limbs. SMFBIA failed to track acute changes in TBW. Divergent impedance changes suggest the trunk is influenced by fluid volume, but the limbs are influenced by ion concentration.

Fat-free mass (FFM) estimation has dramatic importance for body composition evaluation, often providing a basis for treatment of obesity and muscular dystrophy. However, current methods of FFM estimation have several drawbacks, usually related to either cost-effectiveness and equipment size (dual-energy X-ray absorptiometry (DEXA) scan) or model limitations. In this study, we present and validate a new FFM estimation model based on hand-to-hand bioimpedance analysis (BIA) and arm volume. Forty-two participants underwent a full-body DEXA scan, a series of anthropometric measurements, and upper-body BIA measurements with the custom-designed wearable wrist-worn impedance meter. A new two truncated cones (TTC) model was trained on DEXA data and achieved the best performance metrics of 0.886 ± 0.051 r², 0.052 ± 0.009 % mean average error, and 6.884 ± 1.283 kg maximal residual error in FFM estimation. The model further demonstrated its effectiveness in Bland-Altman comparisons with the skinfold thickness-based FFM estimation method, achieving the least mean bias (0.007 kg). The novel TTC model can provide an alternative to full-body BIA measurements, demonstrating an accurate FFM estimation independently of population variables.