Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.
17 Jun 2014-Vol. 2014, pp 381251-381251
TL;DR: The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.
Abstract: Under the alternating electrical excitation, biological tissues produce a complex electrical impedance which depends on tissue composition, structures, health status, and applied signal frequency, and hence the bioelectrical impedance methods can be utilized for noninvasive tissue characterization. As the impedance responses of these tissue parameters vary with frequencies of the applied signal, the impedance analysis conducted over a wide frequency band provides more information about the tissue interiors which help us to better understand the biological tissues anatomy, physiology, and pathology. Over past few decades, a number of impedance based noninvasive tissue characterization techniques such as bioelectrical impedance analysis (BIA), electrical impedance spectroscopy (EIS), electrical impedance plethysmography (IPG), impedance cardiography (ICG), and electrical impedance tomography (EIT) have been proposed and a lot of research works have been conducted on these methods for noninvasive tissue characterization and disease diagnosis. In this paper BIA, EIS, IPG, ICG, and EIT techniques and their applications in different fields have been reviewed and technical perspective of these impedance methods has been presented. The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.
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TL;DR: In this article, the authors used support vector machines (SVMs) and backpropagation neural networks (BPNNs) to classify raw poultry breast myopathies using their BIA patterns.
Abstract: Breast meat from modern fast-growing big birds is affected with myopathies such as woody breast (WB), white striping, and spaghetti meat (SM). The detection and separation of the myopathy-affected meat can be carried out at processing plants using technologies such as bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy-affected breast meat are extremely complicated, especially because of the overlap of these myopathies in individual breast fillets and the human error associated with the assignment of fillet categories. Previous research has shown that traditional statistical techniques such as ANOVA and regression, among others, are insufficient in categorising fillets affected with myopathies by BIA. Therefore, more complex data analysis tools can be used, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), to classify raw poultry breast myopathies using their BIA patterns, such that the technology can be beneficial for the poultry industry in detecting myopathies. Freshly deboned (3-3.5 h post slaughter) breast fillets (n = 100 × 3 flocks) were analysed by hand palpation for WB (0-normal; 1-mild; 2-moderate; 3-Severe) and SM (presence and absence) categorisation. BIA data (resistance and reactance) were collected on each breast fillet; the algorithm of the equipment calculated protein and fat index. The data were analysed by linear discriminant analysis (LDA), and with SVM and BPNN with 70::30: training::test data set. Compared with the LDA analysis, SVM separated WB with a higher accuracy of 71.04% for normal (data for normal and mild merged), 59.99% for moderate, and 81.48% for severe WB. Compared with SVM, the BPNN training model accurately (100%) separated normal WB fillets with and without SM, demonstrating the ability of BIA to detect SM. Supervised learning algorithms, such as SVM and BPNN, can be combined with BIA and successfully implemented in poultry processing to detect breast fillet myopathies.
3 citations
TL;DR: Bioimpedance parameters showed a consistent decrease associated with the first milk ejection when electrodes were placed on the pumped breast, and wide variation in the magnitude of changes observed means further development of the methodology is needed to ensure reliability.
Abstract: Milk ejection is essential for effective milk removal during breastfeeding and pumping, and for continued milk synthesis. Many women are unable to accurately sense milk ejection to determine whether their infant is receiving milk or, when pumping, to switch the pump to a more effective expression pattern. To determine if changes in bioimpedance parameters are associated with milk ejection in the lactating breast during pumping. 30 lactating women participated in 2 pumping sessions within 2 weeks of each other. During pumping the breasts were monitored with bioimpedance spectroscopy (on either the pumped or the non- pumped breast), and milk flow rate and volume were measured simultaneously. All mothers completed 24-h milk productions. Linear mixed effects models were used to determine associations between milk flow rate and bioimpedance changes. Changes in bioimpedance parameters were greater at the first milk ejection when measured on the pumped breast (median (IQR): R zero: −7 (−17, −4,) % (n = 30); R infinity: −8 (−20, −2) % (n = 29); membrane capacitance: −24 (−59, −7) % (n = 27). Changes in bioimpedance detected in the non-pumped breast were lower at the first milk ejection, R zero: −3 (−8, −2) % (n = 25); R infinity: −5 (−8, −2) % (n = 23); membrane capacitance: −9 (−17, 15) % (n = 24). Smaller less consistent decreases in the bioimpedance characteristics were detected at the second milk ejection in both breasts. Bioimpedance parameters showed a consistent decrease associated with the first milk ejection when electrodes were placed on the pumped breast. Smaller decreases were observed when the non-pumped breast was monitored for the first and second milk ejection. There was wide variation in the magnitude of changes observed, and hence further development of the methodology is needed to ensure reliability.
3 citations
01 Oct 2020
TL;DR: The test results show that the integrated BIA-BMI works well and is developed as a chair equipped with several system blocks, namely BIA block, BMI block, power supply block, and microcontroller block.
Abstract: This research was conducted with the aim to build integration between Bioimpedance Analysis (BIA) and Body Mass Index (BMI) for users with special needs. The proposed system can measure height, weight, BMI and body composition simultaneously to be used by the elderly population and handicapped users. The proposed system is developed as a chair equipped with several system blocks, namely BIA block, BMI block, power supply block, and microcontroller block. Before starting the measurement, users only need to enter their age and gender data. The whole system is controlled by using Arduino Mega 2560 on the microcontroller block equipped with keypad for data input and an LCD to display measurement results. System testing is performed by comparing the measurement results with Omron HBF-375. The test involved 8 volunteers (4 males and 4 females). The test results show that the integrated BIA-BMI works well with an average error of 1.5%.
3 citations
Cites background from "Bioelectrical Impedance Methods for..."
...Where BFP is in percentage, FM is in kilograms and m is weight (kg)[20][21]....
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...Where FM is in kilograms, m is weight (kg), and FFM is in kilograms[20][21]....
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...Where FFMfemale is FFM of female (kg), FFMmale is FFM of male (kg), h is height (cm), Z is body impedance (Ohms) and m is weight (kg) [19][20]....
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03 May 2019
TL;DR: In this paper, the authors proposed new linear regression models for total body water (TBW) measurements for both male and female human being based on bioelectrical impedance analysis and found that the proposed models have been analyzed statistically and the results show that the correlation (Pearson) coefficients are 0.998 (p < 0.001) for male and 0.997 (p< 0.002) for female individuals which denote excellent matching with actual data.
Abstract: This work proposes new linear regression models for total body water (TBW) measurements for both male and female human being based on bioelectrical impedance analysis. In this research total 2817 (1397 male and 1420 female) data have been used. Age, height, bioelectrical impedance at 100 kHz frequency, body mass index (BMI) have been used for the development of mathematical models. The proposed models have been analyzed statistically and the results show that the correlation (Pearson) coefficients are 0.998 (p<0.001) for male and 0.997 (p<0.001) for female individuals which denote excellent matching with actual data. Besides intervals of LOA is only -1.36 L to 1.59 L and -1.17 L to 1.12 L for male and female data respectively and most of the data follows such a small limit of agreement. The absolute errors (mean ± Standard Deviation) are (0.64 ± 0.41) L and (0.48 ± 0.32) L for male and female data respectively whereas the bias are 0.11 L for male and -0.02 L for female population only. The RMSE are also very low and which are 0.76 L for male and 0.58 L for female people. Comparing the results of this research with existing models it is seen that the proposed models can be more suitable for TBW measurement.
2 citations
Cites methods from "Bioelectrical Impedance Methods for..."
...Bioelectrical impedance analysis is a tetra electrodes based method in which two electrodes are connected on right hand and another two electrodes are connected in right foot [5]....
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18 Apr 2018
TL;DR: The arterial compliance is a potentially useful hemodynamic parameter which could be successfully used to predict or detect the presence of heart diseases.
Abstract: Introduction: Thoracic bioimpedance is a simple, cost-effective, and non-invasive tool used generally to determine several hemodynamic parameters. Based on this technique, the cardiac output and the stroke volume are the most common parameters used for diagnosing heart diseases. This study aims to investigate the ability of the arterial compliance parameter to predict or detect the presence of heart diseases. Methods: two groups of young subjects participated in this study: a control group consisted of 10 subjects including 2 athletes and a group of 10 patients with various heart diseases. The thoracic bioimpedance recordings are used to determine the arterial compliance for the 2 groups of subjects. Statistical analyses are performed using the Student’s t-test and the ROC curve analysis. Results: Experimental results show that the arterial compliance is significantly lower in patients with heart diseases compared to control subjects (P < 0.001). Conclusions: The arterial compliance is a potentially useful hemodynamic parameter which could be successfully used to predict or detect the presence of heart diseases.
2 citations
Cites background from "Bioelectrical Impedance Methods for..."
...The impedance varies with tissue composition, tissue structure and tissue health status [8]....
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References
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01 Nov 2011TL;DR: In this paper, the authors focus on topics at the forefront of electrochemical research, such as splitting water by electrolysis, splitting water with visible light, and the recent development of lithium batteries.
Abstract: This book focuses on topics at the forefront of electrochemical research. Splitting water by electrolysis; splitting water by visible light; the recent development of lithium batteries; theoretical approaches to intercalation; and fundamental concepts of electrode kinetics, particularly as applied to semiconductors are discussed. It is recommended for electrochemists, physical chemists, corrosion scientists, and those working in the fields of analytical chemistry, surface and colloid science, materials science, electrical engineering, and chemical engineering.
5,927 citations
04 Apr 2005
Abstract: Preface. Preface to the First Edition. Contributors. Contributors to the First Edition. Chapter 1. Fundamentals of Impedance Spectroscopy (J.Ross Macdonald and William B. Johnson). 1.1. Background, Basic Definitions, and History. 1.1.1 The Importance of Interfaces. 1.1.2 The Basic Impedance Spectroscopy Experiment. 1.1.3 Response to a Small-Signal Stimulus in the Frequency Domain. 1.1.4 Impedance-Related Functions. 1.1.5 Early History. 1.2. Advantages and Limitations. 1.2.1 Differences Between Solid State and Aqueous Electrochemistry. 1.3. Elementary Analysis of Impedance Spectra. 1.3.1 Physical Models for Equivalent Circuit Elements. 1.3.2 Simple RC Circuits. 1.3.3 Analysis of Single Impedance Arcs. 1.4. Selected Applications of IS. Chapter 2. Theory (Ian D. Raistrick, Donald R. Franceschetti, and J. Ross Macdonald). 2.1. The Electrical Analogs of Physical and Chemical Processes. 2.1.1 Introduction. 2.1.2 The Electrical Properties of Bulk Homogeneous Phases. 2.1.2.1 Introduction. 2.1.2.2 Dielectric Relaxation in Materials with a Single Time Constant. 2.1.2.3 Distributions of Relaxation Times. 2.1.2.4 Conductivity and Diffusion in Electrolytes. 2.1.2.5 Conductivity and Diffusion-a Statistical Description. 2.1.2.6 Migration in the Absence of Concentration Gradients. 2.1.2.7 Transport in Disordered Media. 2.1.3 Mass and Charge Transport in the Presence of Concentration Gradients. 2.1.3.1 Diffusion. 2.1.3.2 Mixed Electronic-Ionic Conductors. 2.1.3.3 Concentration Polarization. 2.1.4 Interfaces and Boundary Conditions. 2.1.4.1 Reversible and Irreversible Interfaces. 2.1.4.2 Polarizable Electrodes. 2.1.4.3 Adsorption at the Electrode-Electrolyte Interface. 2.1.4.4 Charge Transfer at the Electrode-Electrolyte Interface. 2.1.5 Grain Boundary Effects. 2.1.6 Current Distribution, Porous and Rough Electrodes- the Effect of Geometry. 2.1.6.1 Current Distribution Problems. 2.1.6.2 Rough and Porous Electrodes. 2.2. Physical and Electrochemical Models. 2.2.1 The Modeling of Electrochemical Systems. 2.2.2 Equivalent Circuits. 2.2.2.1 Unification of Immitance Responses. 2.2.2.2 Distributed Circuit Elements. 2.2.2.3 Ambiguous Circuits. 2.2.3 Modeling Results. 2.2.3.1 Introduction. 2.2.3.2 Supported Situations. 2.2.3.3 Unsupported Situations: Theoretical Models. 2.2.3.4 Unsupported Situations: Equivalent Network Models. 2.2.3.5 Unsupported Situations: Empirical and Semiempirical Models. Chapter 3. Measuring Techniques and Data Analysis. 3.1. Impedance Measurement Techniques (Michael C. H. McKubre and Digby D. Macdonald). 3.1.1 Introduction. 3.1.2 Frequency Domain Methods. 3.1.2.1 Audio Frequency Bridges. 3.1.2.2 Transformer Ratio Arm Bridges. 3.1.2.3 Berberian-Cole Bridge. 3.1.2.4 Considerations of Potentiostatic Control. 3.1.2.5 Oscilloscopic Methods for Direct Measurement. 3.1.2.6 Phase-Sensitive Detection for Direct Measurement. 3.1.2.7 Automated Frequency Response Analysis. 3.1.2.8 Automated Impedance Analyzers. 3.1.2.9 The Use of Kramers-Kronig Transforms. 3.1.2.10 Spectrum Analyzers. 3.1.3 Time Domain Methods. 3.1.3.1 Introduction. 3.1.3.2 Analog-to-Digital (A/D) Conversion. 3.1.3.3 Computer Interfacing. 3.1.3.4 Digital Signal Processing. 3.1.4 Conclusions. 3.2. Commercially Available Impedance Measurement Systems (Brian Sayers). 3.2.1 Electrochemical Impedance Measurement Systems. 3.2.1.1 System Configuration. 3.2.1.2 Why Use a Potentiostat? 3.2.1.3 Measurements Using 2, 3 or 4-Terminal Techniques. 3.2.1.4 Measurement Resolution and Accuracy. 3.2.1.5 Single Sine and FFT Measurement Techniques. 3.2.1.6 Multielectrode Techniques. 3.2.1.7 Effects of Connections and Input Impedance. 3.2.1.8 Verification of Measurement Performance. 3.2.1.9 Floating Measurement Techniques. 3.2.1.10 Multichannel Techniques. 3.2.2 Materials Impedance Measurement Systems. 3.2.2.1 System Configuration. 3.2.2.2 Measurement of Low Impedance Materials. 3.2.2.3 Measurement of High Impedance Materials. 3.2.2.4 Reference Techniques. 3.2.2.5 Normalization Techniques. 3.2.2.6 High Voltage Measurement Techniques. 3.2.2.7 Temperature Control. 3.2.2.8 Sample Holder Considerations. 3.3. Data Analysis (J. Ross Macdonald). 3.3.1 Data Presentation and Adjustment. 3.3.1.1 Previous Approaches. 3.3.1.2 Three-Dimensional Perspective Plotting. 3.3.1.3 Treatment of Anomalies. 3.3.2 Data Analysis Methods. 3.3.2.1 Simple Methods. 3.3.2.2 Complex Nonlinear Least Squares. 3.3.2.3 Weighting. 3.3.2.4 Which Impedance-Related Function to Fit? 3.3.2.5 The Question of "What to Fit" Revisited. 3.3.2.6 Deconvolution Approaches. 3.3.2.7 Examples of CNLS Fitting. 3.3.2.8 Summary and Simple Characterization Example. Chapter 4. Applications of Impedance Spectroscopy. 4.1. Characterization of Materials (N. Bonanos, B. C. H. Steele, and E. P. Butler). 4.1.1 Microstructural Models for Impedance Spectra of Materials. 4.1.1.1 Introduction. 4.1.1.2 Layer Models. 4.1.1.3 Effective Medium Models. 4.1.1.4 Modeling of Composite Electrodes. 4.1.2 Experimental Techniques. 4.1.2.1 Introduction. 4.1.2.2 Measurement Systems. 4.1.2.3 Sample Preparation-Electrodes. 4.1.2.4 Problems Associated With the Measurement of Electrode Properties. 4.1.3 Interpretation of the Impedance Spectra of Ionic Conductors and Interfaces. 4.1.3.1 Introduction. 4.1.3.2 Characterization of Grain Boundaries by IS. 4.1.3.3 Characterization of Two-Phase Dispersions by IS. 4.1.3.4 Impedance Spectra of Unusual Two-phase Systems. 4.1.3.5 Impedance Spectra of Composite Electrodes. 4.1.3.6 Closing Remarks. 4.2. Characterization of the Electrical Response of High Resistivity Ionic and Dielectric Solid Materials by Immittance Spectroscopy (J. Ross Macdonald). 4.2.1 Introduction. 4.2.2 Types of Dispersive Response Models: Strengths and Weaknesses. 4.2.2.1 Overview. 4.2.2.2 Variable-slope Models. 4.2.2.3 Composite Models. 4.2.3 Illustration of Typical Data Fitting Results for an Ionic Conductor. 4.3. Solid State Devices (William B. Johnson and Wayne L. Worrell). 4.3.1 Electrolyte-Insulator-Semiconductor (EIS) Sensors. 4.3.2 Solid Electrolyte Chemical Sensors. 4.3.3 Photoelectrochemical Solar Cells. 4.3.4 Impedance Response of Electrochromic Materials and Devices (Gunnar A. Niklasson, Anna Karin Johsson, and Maria Stromme). 4.3.4.1 Introduction. 4.3.4.2 Materials. 4.3.4.3 Experimental Techniques. 4.3.4.4 Experimental Results on Single Materials. 4.3.4.5 Experimental Results on Electrochromic Devices. 4.3.4.6 Conclusions and Outlook. 4.3.5 Time-Resolved Photocurrent Generation (Albert Goossens). 4.3.5.1 Introduction-Semiconductors. 4.3.5.2 Steady-State Photocurrents. 4.3.5.3 Time-of-Flight. 4.3.5.4 Intensity-Modulated Photocurrent Spectroscopy. 4.3.5.5 Final Remarks. 4.4. Corrosion of Materials (Digby D. Macdonald and Michael C. H. McKubre). 4.4.1 Introduction. 4.4.2 Fundamentals. 4.4.3 Measurement of Corrosion Rate. 4.4.4 Harmonic Analysis. 4.4.5 Kramer-Kronig Transforms. 4.4.6 Corrosion Mechanisms. 4.4.6.1 Active Dissolution. 4.4.6.2 Active-Passive Transition. 4.4.6.3 The Passive State. 4.4.7 Point Defect Model of the Passive State (Digby D. Macdonald). 4.4.7.1 Introduction. 4.4.7.2 Point Defect Model. 4.4.7.3 Electrochemical Impedance Spectroscopy. 4.4.7.4 Bilayer Passive Films. 4.4.8 Equivalent Circuit Analysis (Digby D. Macdonald and Michael C. H. McKubre). 4.4.8.1 Coatings. 4.4.9 Other Impedance Techniques. 4.4.9.1 Electrochemical Hydrodynamic Impedance (EHI). 4.4.9.2 Fracture Transfer Function (FTF). 4.4.9.3 Electrochemical Mechanical Impedance. 4.5. Electrochemical Power Sources. 4.5.1 Special Aspects of Impedance Modeling of Power Sources (Evgenij Barsoukov). 4.5.1.1 Intrinsic Relation Between Impedance Properties and Power Sources Performance. 4.5.1.2 Linear Time-Domain Modeling Based on Impedance Models, Laplace Transform. 4.5.1.3 Expressing Model Parameters in Electrical Terms, Limiting Resistances and Capacitances of Distributed Elements. 4.5.1.4 Discretization of Distributed Elements, Augmenting Equivalent Circuits. 4.5.1.5 Nonlinear Time-Domain Modeling of Power Sources Based on Impedance Models. 4.5.1.6 Special Kinds of Impedance Measurement Possible with Power Sources-Passive Load Excitation and Load Interrupt. 4.5.2 Batteries (Evgenij Barsoukov). 4.5.2.1 Generic Approach to Battery Impedance Modeling. 4.5.2.2 Lead Acid Batteries. 4.5.2.3 Nickel Cadmium Batteries. 4.5.2.4 Nickel Metal-hydride Batteries. 4.5.2.5 Li-ion Batteries. 4.5.3 Impedance Behavior of Electrochemical Supercapacitors and Porous Electrodes (Brian E. Conway). 4.5.3.1 Introduction. 4.5.3.2 The Time Factor in Capacitance Charge or Discharge. 4.5.3.3 Nyquist (or Argand) Complex-Plane Plots for Representation of Impedance Behavior. 4.5.3.4 Bode Plots of Impedance Parameters for Capacitors. 4.5.3.5 Hierarchy of Equivalent Circuits and Representation of Electrochemical Capacitor Behavior. 4.5.3.6 Impedance and Voltammetry Behavior of Brush Electrode Models of Porous Electrodes. 4.5.3.7 Impedance Behavior of Supercapacitors Based on Pseudocapacitance. 4.5.3.8 Deviations of Double-layer Capacitance from Ideal Behavior: Representation by a Constant-phase Element (CPE). 4.5.4 Fuel Cells (Norbert Wagner). 4.5.4.1 Introduction. 4.5.4.2 Alkaline Fuel Cells (AFC). 4.5.4.3 Polymer Electrolyte Fuel Cells (PEFC). 4.5.4.4 Solid Oxide Fuel Cells (SOFC). Appendix. Abbreviations and Definitions of Models. References. Index.
5,212 citations
TL;DR: In this article, a CO2-laser-based photoacoustic spectrometer was used to determine the temporal concentration profile of atmospheric ethene in Mexico City, and the results of this campaign were compared with data obtained in the winter of 2001.
Abstract: A CO2-laser-based photoacoustic spectrometer was used to determine the temporal concentration profile of atmospheric ethene in Mexico City. Ethene measurements were conducted at the facilities of our institute, which is located in the north of the city and next to an avenue with heavy traffic density. Ambient air from outside our laboratory was continuously pumped into the spectrometer. This campaign was performed for 24 h a day, from November 24–30, 2001. The maximum ethene levels ranged between 26 and 81 ppbV. As expected, the lowest concentrations were monitored on weekends. These data were analyzed in combination with ozone and nitrogen oxides profiles, which were permanently monitored by an air-pollution-monitoring government network. Information on the seasonal variability of ethene was obtained by comparing the results of this campaign with data obtained in the winter of 2001. In general, the ethene concentration in November was about 30% higher than in February. On weekdays, the mean dose of human...
3,242 citations
TL;DR: In this article, a new analysis tool was developed to quantify the experimentally observed changes in morphology of portlandite, allowing the calculation of the relative surface energies of the crystal facets.
2,498 citations