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: Comparing bio-impedance spectroscopy and CMR for the assessment of thoracic fluid status, and to determine their ability to detect fluid displacement induced by a leg compression procedure in healthy volunteers, shows good correlation was observed between the two measurement techniques.
Abstract: Heart failure is marked by frequent hospital admissions, often as a consequence of pulmonary congestion. Current gold standard techniques for thoracic fluid measurement require invasive heamodynamic access and therefore they are not suitable for continuous monitoring. Changes in thoracic impedance (TI) may enable non-invasive early detection of congestion and prevention of unplanned hospitalizations. However, the usefulness of TI to assess thoracic fluid status is limited by inter-subject variability and by the lack of reliable normalization methods. Indicator dilution methods allow absolute fluid volume estimation; cardiac magnetic resonance (CMR) has been recently proposed to apply indicator dilution methods in a minimally-invasive manner. In this study, we aim to compare bio-impedance spectroscopy (BIS) and CMR for the assessment of thoracic fluid status, and to determine their ability to detect fluid displacement induced by a leg compression procedure in healthy volunteers. A pressure gradient was applied across each subject's legs for 5 min (100-60 mmHg, distal to proximal). Each subject underwent a continuous TI-BIS measurement during the procedure, and repeated CMR-based indicator dilution measurements on a 1.5 T scanner at baseline, during compression, and after pressure release. The Cole-Cole and the local density random walk models were used for parameter extraction from TI-BIS and indicator dilution measurements, respectively. Intra-thoracic blood volume index (ITBI) derived from CMR, and extracellular fluid resistance (R E) from TI-BIS, were considered as thoracic fluid status measures. Eight healthy volunteers were included in this study. An increase in ITBI of 45.2 ± 47.2 ml m-2 was observed after the leg inflation (13.1 ± 15.1% w.r.t. baseline, p < 0.05), while a decrease of -0.84 ± 0.39 Ω in R E (-1.7 ± 0.9% w.r.t. baseline, p < 0.05) was observed. ITBV and R E normalized by body mass index were strongly inversely correlated (r = -0.93, p < 0.05). In conclusion, an acute fluid displacement to the thoracic circulation was induced in healthy volunteers. Significant changes were observed in the considered thoracic fluid measures derived from BIS and CMR. Good correlation was observed between the two measurement techniques. Further clinical studies will be necessary to prospectively evaluate the value of a combination of the two techniques for prediction of re-hospitalizations after admission for heart failure.
5 citations
01 Feb 2021
TL;DR: The aim of the present study was to compare the difference between calculated RMR using various predictive formulas reported in the literature and the values from measured iCal, and showed that the prevalent equations under-rather than overestimated the RMR.
Abstract: Summary Background & aims Resting metabolic rate (RMR) can either be estimated using specific equations or measured with indirect calorimetry (iCal). The calculation formulas usually exclude body composition. In addition to age, height, weight, sex and disease, fat-free mass (FFM) and fat mass (FM), in particular, influence energy expenditure. The aim of the present study was to compare the difference between calculated RMR using various predictive formulas reported in the literature and the values from measured iCal. Additionally, differing muscle mass and FM were considered in the calculations. Our hypothesis was that the accuracy of the calculation formulas depends on body constitution. Methods This monocentric exploratory pilot study with an observational cross-sectional design included 69 healthy normal-weight adults. Participants were assigned to groups depending on constitution (slender, athletic, full slender). RMR was estimated with a set of 12 clinically relevant predictive equations. Results The comparison of the calculated and measured values showed that the prevalent equations under-rather than overestimated the RMR. The equation from the World Health Organization (WHO) had the most accurate prediction range, from 90% to 110% of the RMR measured by iCal, followed by the Harris–Benedict formula and its modifications according to Roza–Shizgal and Mifflin. The agreement between predicting formulas and measured RMR improved in the group with higher fat mass. Conclusions Body composition has a significant influence on RMR, mainly through muscle mass. The equations used correlated well with iCal in only 40%–60% of the studied population. Measurements and calculations varied considerably for subjects with greater muscle mass, whereas they were more consistent for subjects with greater FM. Increased emphasis should be placed on the measurement of RMR using iCal.
5 citations
01 Apr 2017
TL;DR: In this paper, the authors proposed a bio-impedance analysis (BIA) tool capable to body composition assessment, which uses four electrodes, two of which are used for 50 kHz sine wave current flow to the body and the rest are used to measure the voltage produced by the body for impedance analysis.
Abstract: Common weight scales cannot assess body composition or determine fat mass and fat-fress mass that make up the body weight. This research propose bio-impedance analysis (BIA) tool capable to body composition assessment. This tool uses four electrodes, two of which are used for 50 kHz sine wave current flow to the body and the rest are used to measure the voltage produced by the body for impedance analysis. Parameters such as height, weight, age, and gender are provided individually. These parameters together with impedance measurements are then in the process to produce a body fat percentage. The experimental result shows impressive repeatability for successive measurements (stdev ≤ 0.25% fat mass). Moreover, result on the hand to hand node scheme reveals average absolute difference of total subjects between two analyzer tools of 0.48% (fat mass) with maximum absolute discrepancy of 1.22% (fat mass). On the other hand, the relative error normalized to Omron's HBF-306 as comparison tool reveals less than 2% relative error. As a result, the system performance offers good evaluation tool for fat mass in the body.
5 citations
16 Nov 2020
TL;DR: In this article, the authors used the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis.
Abstract: The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson’s correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.
5 citations
Journal Article•
TL;DR: How to take good ultrasonic pictures of the breast and make correct diagnosis are discussed and diagnostic criteria preliminarily adopted by the Japan Society of Ultrasonics in Medicine are proposed.
Abstract: In this paper, first, how to take good ultrasonic pictures of the breast and make correct diagnosis are discussed. The author stressed the earnest attitude to understand and master the apparatus and correlate ultrasonic pictures with cut surfaces or microscopical figures of the specimen. Secondly, diagnostic criteria preliminarily adopted by the Japan Society of Ultrasonics in Medicine are proposed and are explained practically. The elements of this criteria are shape, border, boundary echoes, internal echoes, posterior echoes, bilateral shadows, changes at surrounding tissues and longitudinal-transverse ratio. Lastly, pictures of the benign and malignant lesions of the breast are shown using typical examples.
5 citations
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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