Journal of Electrical Bioimpedance最新文献

Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter that is present at a relatively low level throughout the normal adult human brain. Abnormal GABA levels are found in people with neurodegenerative disorders such as Parkinson's disease, epilepsy, schizophrenia, depression, and others. Being able to measure the GABA concentration would be beneficial for patient groups with fluctuating GABA levels for better diagnosis and treatment. In this study, we explore the feasibility of using dielectric relaxation spectroscopy for the detection of GABA concentrations within a physiological range, with the perspective of miniaturization and use during implantation. Utilizing machine learning techniques, we were able to differentiate GABA concentrations down to 5 μm. This work investigates a novel use of dielectric relaxation spectroscopy, to assess if physiological GABA concentrations can be detected through permittivity measurements.

Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain's electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters-namely Activity, Mobility, and Complexity-is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics.

Ultrasound guided peripheral nerve block (USgPNB) refers to anaesthetic techniques to deposit local anesthetic next to nerves, permitting painful surgery without necessitating general anesthesia. Needle tip position prior to local anesthetic deposition is a key determinant of block success and safety. Nerve puncture and intra-neural injection of local anesthetic can cause permanent nerve injury. Currently ultrasound guidance is not sufficiently sensitive to reliably detect needle to nerve proximity. Feedback with bioimpedance data from the smart needle tip might provide the anesthetist with information as to the relationship between the needle tip and the target nerve prior to local anesthetic deposition. Bioimpedance using a smart needle integrated with a two-electrode impedance sensor has been developed to determine needle to nerve proximity during USgPNB. Having obtained all necessary ethical and regulatory approvals, in vivo data on brachial plexus, vagus, femoral and sciatic nerves were obtained from seven pig models using the smart needle bioimpedance system. The excision and histological analysis of above peripheral nerves and observation of the architecture and structure of nerves by means of histology allow the calculation of the ratios of connective tissue to neural tissue to determine the influence of this variable on absolute impedance. The ratio results give extra clinical data and explain the hetrogeneity of impedance data in the pig models and the hypothesis that connective tissue with intra-neural fat has higher impedance than neural tissue.

Background: Bioimpedance derived whole body phase angle (ϕ), a measure of cellular integrity, has been identified as an independent marker of morbidity and mortality in many medical and surgical specialties. While similar measures of water homeostasis like extracellular edema (EE) have been associated with pre-operative risk, ϕ has not been studied in orthopaedics, despite potential to serve as a pre-operative marker. This study aims to identify relationships between ϕ, EE, and body composition metrics, laboratory values, patient reported outcomes, and comorbidities.

Methods: Multi-frequency bioimpedance analysis (BIA) records, laboratory values, and patient reported outcomes of adult patients presenting to an academic arthroplasty clinic were retrospectively reviewed. Correlation coefficients between ϕ, EE, and reviewed information were conducted.

Results: ϕ was significantly correlated (p<0.001) most positively with measures of lean tissue such as skeletal muscle mass (r=0.48), appendicular skeletal muscle index (r=0.39), lean body mass (r=0.43), and dry lean mass (r=0.47), while it held negative correlations (p<0.001) with age (r= -0.55), and body fat mass (r= -0.11). ϕ was not correlated with body mass index (BMI, p = 0.204). In contrast, EE demonstrated its strongest positive correlations (p<0.001) with body fat mass (r=0.32), age (r=0.50), and BMI (r=0.26), and its strongest negative correlations (p<0.001) with serum albumin (r= -0.37) and total protein (r= -0.23).

Conclusions: Based on their associations with markers of health and fitness, BIA determined ϕ and EE demonstrate relationships to markers currently implemented in orthopaedic practice. This likely indicates that ϕ has potential as a comprehensive surrogate for several commonly used markers to quantify pre-operative risk. In the future, ϕ may aid in developing risk-stratifications for intervention and prevention of complications.

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.