Frequency-domain reconstruction of signals in electrical bioimpedance spectroscopy

A hybrid Genetic Algorithm and Levenberg-Marquardt (GA-LM) method for cell suspension measurement with electrical impedance spectroscopy

A hybrid Genetic Algorithm (GA) and Levenberg-Marquardt (GA-LM) method is proposed for cell suspension measurement with electrical impedance spectroscopy. This algorithm combines the GA with global search ability and Levenberg-Marquardt (LM) algorithm with local search ability, which has the advantages of high accuracy and high robustness. First, GA-LM is compared with GA and LM algorithm separately by ideal simulation. Second, Gaussian noise is added to the ideal simulation data. The anti-noise ability of the GA-LM is discussed. Finally, experiments are conducted to verify the practicability of the proposed GA-LM method. In the experiment, GA-LM is used to fit the impedance spectrum of yeast suspensions with different volume fractions and active states. The results show that the GA-LM algorithm can converge to the real value that is set in the simulation under ideal numerical simulation conditions. In the simulation within 2% noise level, the mean relative error of the parameter solution is less than 4%, and the root mean square error of the fitting is less than 0.4. This method also performs well in fitting of the experimental data. In addition, the electric double layer resistance and cell membrane capacitance are selected as the main indicators for the identification of yeast suspension concentration and activity, respectively.

Extraction of bioimpedance phase information from its magnitude using a non-uniform Kramers-Kronig transform

A novel non-uniform Kramers-Kronig Transform algorithm for bioimpedance phase extraction is proposed and tested in this work. The algorithm error is studied and compared with a previously proposed phase extraction technique, also based on the Kramers-Kronig transform. Results using simulated datasets and experimental datasets confirm the excellent performance of the algorithm.

In silico validation procedure for cell volume fraction estimation through dielectric spectroscopy

Dielectric spectroscopy has proved to be a good tool for analyzing the passive electrical properties of biological tissues as well as those of inhomogeneous materials. This technique promises to be a valid alternative to the classical ones based on metabolites to monitor the growth and cell volume fraction of cell cultures in a simple and minimally invasive way. In order to obtain an accurate estimation of the cell volume fraction as a function of the permittivity of the suspension, a simple in silico procedure is proposed. The procedure is designed to perform homogenization from the micro-scale to the macro-scale using simple analytical models and simulation setups hypothesizing the properties of diluted suspension (cell volume fraction less than 0.2). Results obtained show the possibility to overcome some trouble involving the analytical treatment of the cellular shape by considering a sphere with the same permittivity in the quantitative analysis of the cell volume fraction. The entire study is based on computer simulations performed in order to verify the correctness of the procedure. Obtained data are used in a cell volume fraction estimation scenario to show the effectiveness of the procedure.

Extracting the parameters of the double-dispersion Cole bioimpedance model from magnitude response measurements

In the field of bioimpedance measurements, the Cole impedance model is widely used for characterizing biological tissues and biochemical materials. In this work, a nonlinear least squares fitting is applied to extract the double-dispersion Cole impedance parameters from simulated magnitude response datasets without requiring the direct impedance data or phase information. The technique is applied to extract the impedance parameters from MATLAB simulated noisy magnitude datasets showing less than 1.2 % relative error when 60 dB SNR Gaussian white noise is present. This extraction is verified experimentally using apples as the Cole impedances showing less than 3 % relative error between simulated responses (using the extracted impedance parameters) and the experimental results over the entire dataset.

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.

Model-based correction of the influence of body position on continuous segmental and hand-to-foot bioimpedance measurements

Bioimpedance spectroscopy (BIS) is suitable for continuous monitoring of body water content. The combination of body posture and time is a well-known source of error, which limits the accuracy and therapeutic validity of BIS measurements. This study evaluates a model-based correction as a possible solution. For this purpose, an 11-cylinder model representing body impedance distribution is used. Each cylinder contains a nonlinear two-pool model to describe fluid redistribution due to changing body position and its influence on segmental and hand-to-foot (HF) bioimpedance measurements. A model-based correction of segmental (thigh) and HF measurements (Xitron Hydra 4200) in nine healthy human subjects (following a sequence of 7 min supine, 20 min standing, 40 min supine) has been evaluated. The model-based compensation algorithm represents a compromise between accuracy and simplicity, and reduces the influence of changes in body position on the measured extracellular resistance and extracellular fluid by up to 75 and 70%, respectively.

A novel bioengineering clinical device testing cellular homeostatic potentials to customize and monitor nutritional-related age-management interventional strategies

Most devices assessing body composition harbor a number of drawbacks and hardly assess the phenomena taking place at a cellular membrane level. The present single-frequency bioelectrical potential homeostatic structure analysis (PHoSA) technology requires only a proper hands contact on fixed electrodes and determines the phase displacement between tested current and voltage by using a 50-KHz alternate sinusoidal current. This allows quick testing time with high degree of precision, sensitivity, and specificity of sectorial functional body compartments analysis. Such assessment may prove to be an integrated part of either a diagnostic workup or monitoring tool in tailoring nutritional/nutraceutical, pharmacological, and exercise activity, all being framed within a proactive, preventive, age-intervention management strategy.