Modeling the non-stationary behaviour of time-varying electrical bioimpedance

The electrical bioimpedance (EBI) measurement of varying biological systems Z(¿,t) (e.g. the heart, the lungs,.) by means of electrical impedance spectroscopy (EIS) remains an open challenge today. Briefly stated, the bioimpedance is widely assumed to be time-invariant when it is measured with the frequency sweep EIS approach. Hence, time-varying changes are thus ignored or treated as a noise source. In this work, we attempt to model the time-variant effects and obtain a simple (periodically) time-varying [(P)TV)] electrical circuit model with (P)TV parameters from experimental in vivo EBI data using the model proposed by Fricke- Morse. The aim is then to illustrate that a limited number of harmonic components of the electrical circuit parameters, which corresponds to an integer number of the bio-system periodicity, can be used to have a realistic evolution of the bioimpedance over time as well as in frequency.Peer ReviewedPostprint (published version

ELECTRICAL IMPEDANCE SPECTROSCOPY AND TOMOGRAPHY: APPLICATIONS ON PLANT CHARACTERIZATION

World population will grow to 9.6 billion by 2050 and global food production needs to be increased by 70% to feed the increased population. Hence, better insight into plant physiology can impart better quality in fruits, vegetables, and crops, and eventually contribute to food security and sustainability. In this direction, this thesis utilizes electrical sensing technology, electrical impedance spectroscopy (EIS) and tomography (EIT), for better understanding and characterization of a number of physiological and structural aspects of the plant. It investigates the dehydration process in onion and ripening process in avocado by EIS, and perform 3D structural imaging of root by EIT. The thesis tracks and analyzes the dynamics of natural dehydration in onion and also assesses its moisture content using EIS. The work develops an equivalent electrical circuit that simulates the response of the onion undergoing natural drying for a duration of three weeks. The developed electrical model shows better congruence with the experimental data when compared to other conventional models for plant tissue with a mean absolute error of 0.42% and root mean squared error of 0.55%. Moreover, the study attempts to find a correlation between the measured impedance data and the actual moisture content of the onions under test (measured by weighing) and develops a simple mathematical model. This model provides an alternative tool for assessing the moisture content of onion nondestructively. The model shows excellent correlation with the ground truth data with a deterministic coefficient of 0.977, root mean square error of 0.030 and sum of squared error of 0.013. Next, the thesis presents an approach that will integrate EIS and machine learning technique that allows us to monitor ripening degree of avocado. It is evident from this study that the impedance absolute magnitude of avocado gradually decreases as the ripening stages (firm, breaking, ripe and overripe) proceed at a particular frequency. In addition, Principal component analysis shows that impedance magnitude (two principal components combined explain 99.95% variation) has better discrimination capabilities for ripening degrees compared to impedance phase angle, impedance real part, and impedance imaginary part. The developed classifier utilizes two principal component features over 100 EIS responses and demonstrate classification over firm, breaking, ripe and overripe stages with an accuracy of 90%, precision of 93%, recall of 90%, f1-score of 90% and an area under ROC curve (AUC) of 88%. Later on, this thesis presents the design, development, and implementation of a low-cost EIT system and analyzes root imaging as well. The designed prototype consists of an electrode array system, an Impedance analyzer board, 2 multiplexer units, and an Arduino. The Eval-Ad5933-EBZ is used for measuring the bio-impedance of the root, and two CD74HC4067 Multiplexers are used as electrode switching unit. Measuring and data collecting are controlled by the Arduino, and data storage is performed in a PC. By performing Finite Element Analysis and solving forward and inverse problem, the tomographic image of the root is reconstructed. The system is able to localize and build 2D and 3D tomographic image of root in a liquid medium. This proposed low-cost and easy-to-access system enables the users to capture the repetitive, noninvasive and non-destructive image of a plant root. Furthermore, the study proposes a simple mathematical model, based on ridge regression, which can predict root biomass from EIT data nondestructively with an accuracy of more than 93%. Thus, this study offers plant scientists and crop consultants the ability to better understand plant physiology nondestructively and noninvasively

ELECTRICAL IMPEDANCE SPECTROSCOPY AND TOMOGRAPHY: APPLICATIONS ON PLANT CHARACTERIZATION

World population will grow to 9.6 billion by 2050 and global food production needs to be increased by 70% to feed the increased population. Hence, better insight into plant physiology can impart better quality in fruits, vegetables, and crops, and eventually contribute to food security and sustainability. In this direction, this thesis utilizes electrical sensing technology, electrical impedance spectroscopy (EIS) and tomography (EIT), for better understanding and characterization of a number of physiological and structural aspects of the plant. It investigates the dehydration process in onion and ripening process in avocado by EIS, and perform 3D structural imaging of root by EIT. The thesis tracks and analyzes the dynamics of natural dehydration in onion and also assesses its moisture content using EIS. The work develops an equivalent electrical circuit that simulates the response of the onion undergoing natural drying for a duration of three weeks. The developed electrical model shows better congruence with the experimental data when compared to other conventional models for plant tissue with a mean absolute error of 0.42% and root mean squared error of 0.55%. Moreover, the study attempts to find a correlation between the measured impedance data and the actual moisture content of the onions under test (measured by weighing) and develops a simple mathematical model. This model provides an alternative tool for assessing the moisture content of onion nondestructively. The model shows excellent correlation with the ground truth data with a deterministic coefficient of 0.977, root mean square error of 0.030 and sum of squared error of 0.013. Next, the thesis presents an approach that will integrate EIS and machine learning technique that allows us to monitor ripening degree of avocado. It is evident from this study that the impedance absolute magnitude of avocado gradually decreases as the ripening stages (firm, breaking, ripe and overripe) proceed at a particular frequency. In addition, Principal component analysis shows that impedance magnitude (two principal components combined explain 99.95% variation) has better discrimination capabilities for ripening degrees compared to impedance phase angle, impedance real part, and impedance imaginary part. The developed classifier utilizes two principal component features over 100 EIS responses and demonstrate classification over firm, breaking, ripe and overripe stages with an accuracy of 90%, precision of 93%, recall of 90%, f1-score of 90% and an area under ROC curve (AUC) of 88%. Later on, this thesis presents the design, development, and implementation of a low-cost EIT system and analyzes root imaging as well. The designed prototype consists of an electrode array system, an Impedance analyzer board, 2 multiplexer units, and an Arduino. The Eval-Ad5933-EBZ is used for measuring the bio-impedance of the root, and two CD74HC4067 Multiplexers are used as electrode switching unit. Measuring and data collecting are controlled by the Arduino, and data storage is performed in a PC. By performing Finite Element Analysis and solving forward and inverse problem, the tomographic image of the root is reconstructed. The system is able to localize and build 2D and 3D tomographic image of root in a liquid medium. This proposed low-cost and easy-to-access system enables the users to capture the repetitive, noninvasive and non-destructive image of a plant root. Furthermore, the study proposes a simple mathematical model, based on ridge regression, which can predict root biomass from EIT data nondestructively with an accuracy of more than 93%. Thus, this study offers plant scientists and crop consultants the ability to better understand plant physiology nondestructively and noninvasively

Towards Bio-impedance Based Labs: A Review

In this article, some of the main contributions to BI (Bio-Impedance) parameter-based systems for medical, biological and industrial fields, oriented to develop micro laboratory systems are summarized. These small systems are enabled by the development of new measurement techniques and systems (labs), based on the impedance as biomarker. The electrical properties of the life mater allow the straightforward, low cost and usually non-invasive measurement methods to define its status or value, with the possibility to know its time evolution. This work proposes a review of bio-impedance based methods being employed to develop new LoC (Lab-on-a-Chips) systems, and some open problems identified as main research challenges, such as, the accuracy limits of measurements techniques, the role of the microelectrode-biological impedance modeling in measurements and system portability specifications demanded for many applications.Spanish founded Project: TEC 2013-46242-C3-1-P: Integrated Microsystem for Cell Culture AssaysFEDE

A low-power recursive I/Q signal generator and current driver for bioimpedance applications

This brief presents a power-efficient quadrature signal generator and current driver application-specific integrated circuit (ASIC) for bioimpedance measurements in an electrical impedance tomography system for monitoring lung function. The signal generator is realized by a digital recursive signal oscillator with the ability of generating quadrature signals over a wide frequency range. The generated in-phase signal is applied to a current driver. It uses a balanced current-mode feedback architecture that monitors the output current through a feedback loop to minimize common-mode voltage build-up at the injection site. The quadrature signals can be used for I/Q demodulation of the measured bioimpedance. The ASIC was designed in TSMC 65 nm technology occupying an area of 0.21 mm2. The current driver can generate up to 0.7 mA current up to 200 kHz and consumes 2.7 mW power using ±0.8 V supplies

Energy-Efficient PRBS Impedance Spectroscopy on a Digital Versatile Platform

partially_open6siThis research has been partially funded by the Italian Ministry of University and Research (MUR) through the program “Dipartimenti di Eccellenza” (2018-2022). The research has also received partial support from the Italian Ministry of University and Research (MUR) and the Eranet FLAG ERA initiative within CONVERGENCE project (CUP B84I16000030005) through the IUNET Consortium.This paper presents the digital design of a versatile and low-power broadband impedance spectroscopy (IS) system based on pseudo-random binary sequence (PRBS) excitation. The PRBS technique allows fast, and low-power estimation of the impedance spectrum over a wide bandwidth with adequate accuracy, proving to be a good candidate for portable medical devices, especially. This paper covers the low-power design of the firmware algorithms and implements them on a versatile and reconfigurable digital platform that can be easily adjusted to the specific application. It will analyze the digital platform with the aim of reducing power consumption while maintaining adequate accuracy of the estimated spectrum. The paper studies two main algorithms (time-domain and frequency-domain) used for PRBS-based IS and implements both of them on the ultra-low-power GAP-8 digital platform. They are compared in terms of accuracy, measurement time, and power budget, while general design trade-offs are drawn out. The time-domain algorithm demonstrated the best accuracy while the frequency-domain one contributes more to save power and energy. However, analysis of the energy-per-error FOM revealed that the time-domain algorithm outperforms the frequency-domain algorithm offering better accuracy for the same energy consumption. Numerical methods and microprocessor resources are exploited to optimize the implementation of both algorithms achieving 27 ms in processing time, power consumption as low as 1.4 mW and a minimum energy consumption per measurement of 0.5 mJ, for a dense impedance spectrum estimation of 214 points.embargoed_20210525Luciani G.; Crescentini M.; Romani A.; Chiani M.; Benini L.; Tartagni M.Luciani G.; Crescentini M.; Romani A.; Chiani M.; Benini L.; Tartagni M