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

Biological tissue characterization by magnetic induction spectroscopy (MIS): requirements and limitations

Magnetic induction spectroscopy (MIS) aims at the contactless measurement of the passive electrical properties (PEP) σ, ε, and μ of biological tissues via magnetic fields at multiple frequencies. Whereas previous publications focus on either the conductive or the magnetic aspect of inductive measurements, this article provides a synthesis of both concepts by discussing two different applications with the same measurement system: 1) monitoring of brain edema and 2) the estimation of hepatic iron stores in certain pathologies. We derived the equations to estimate the sensitivity of MIS as a function of the PEP of biological objects. The system requirements and possible systematic errors are analyzed for a MIS-channel using a planar gradiometer (PGRAD) as detector.We studied 4 important error sources: 1) moving conductors near the PGRAD; 2) thermal drifts of the PGRAD-parameters; 3) lateral displacements of the PGRAD; and 4) phase drifts in the receiver. All errors were compared with the desirable resolution. All errors affect the detected imaginary part (mainly related to σ ) of the measured complex field much less than the real part (mainly related to ε and μ). Hence, the presented technique renders possible the resolution of (patho-) physiological changes of the electrical conductivity when applying highly resolving hardware and elaborate signal processing. Changes of the magnetic permeability and permittivity in biological tissues are more complicated to deal with and may require chopping techniques, e.g., periodic movement of the object.Peer Reviewe

Modeling Brain Using Parameters of Passive Electrical Circuits

Brain is the central and most complex organ in the human body. It controls most of the body functions, processing, integrating, and coordinating the information it receives from the organs, and sending decision instructions to the rest of the body. Brain injury may occur due to external environmental and/or internal influences. Timely diagnosing and differentiating the type of brain injury is critical. CT and MRI are often used for the diagnosis, which may not be available at a remote location or at an accident site or an emergency vehicle. This thesis contributes to the modeling of the brain from the electrical point of view, considering the structural complexity of the head composed of biological tissues with different dielectric properties. The goal of the thesis is to develop electrical models of the brain using parameters of passive electrical circuits. The models are developed for a normal brain and a brain with pathological conditions such as edema (swelling) and hemorrhage (bleeding). The circuit models are simulated at a range of frequencies from 1 Hz to 200 kHz. The experiment data is collected on a sheep brain surrounded by phantom tissue using bioimpedance analyzer at a range of frequencies from 1 Hz to 200 kHz. The simulation results are compared with experiment data

Bio-electromagnetic Instrumentation Problems Related to the Human Body

Long bone fractures account for approximately 10% of all non-fatal injuries in the United States. A significant portion of them are due to osteoporosis. The objective of this project is to test different methods for detecting osteoporotic bones, both theoretically and experimentally. They include: 1. Steady-state electric current sensors – electric impedance tomography 2. Infrared scattering sensors 3. Microwave sensor

Tecniche Elettrotomografiche per la caratterizzazione dei tessuti biologici

Electrical impedance tomography (EIT) is an imaging modality wherein the spatial map of conductivity and permittivity inside a medium is obtained from a set of surface electrical measurements. Electrodes are brought into contact with the surface of the object being imaged and a set of currents are applied and the corresponding voltages are measured. These voltages and currents are then used to estimate the electrical properties of the object using an image reconstruction algorithm which relies on an accurate model of the electrical interaction. The process of property estimation, called inverse problem, is highly ill-posed and it requires a Regularization method. The objective of this Thesis was to develop a device for imaging using the EIT technique, which was convenient, noninvasive, easily programmable, portable and relatively cheap in contrast to many other diagnostic tool. In this direction a simple EIT system and its hardware and software parts are developed. The data processing was accomplished by utilizing the EIDORS toolkit, which was developed for application to this nonlinear and ill-posed inverse problem. Experiments have indicated that the EIT system can reconstruct resistive and capacitive images of good contrast despite errors in the measurement are not taken in account

Non-invasive hemodynamic monitoring by electrical impedance tomography

The monitoring of central hemodynamic parameters such as cardiac output (CO) and pulmonary artery pressure (PAP) is of paramount clinical importance to assess the health status of the cardiovascular system. However, their measurement requires the insertion of a pulmonary artery catheter, a highly invasive procedure associated with non-negligible morbidity and mortality rates. In this thesis, we investigated the clinical potential of electrical impedance tomography (EIT) - a radiation-free medical imaging technique - as a non-invasive alternative for the measurement of CO and PAP. In a first phase, we investigated the potential of EIT for the measurement of CO. This measurement is implicitly based on the hypothesis that the EIT heart signal (the ventricular component of the EIT signals) is induced by ventricular blood volume changes. This hypothesis has never been formally investigated, and the exact origins of the EIT heart signal remain subject to interpretation. Therefore, using a model, we investigated the genesis of this signal by identifying its various sources and their respective contributions. The results revealed that the EIT heart signal is dominated by cardioballistic effects (heart motion). However, although of prominently cardioballistic origin, the amplitude of the signal has shown to be strongly correlated to stroke volume (r = 0.996, p < 0.001; error of 0.57 +/- 2.19 mL). We explained these observations by the quasi-incompressibility of myocardial tissue and blood. We further identified several factors and conditions susceptible to affect the accuracy of the measurement. Finally, we investigated the influence of the EIT sensor belt position on the measured heart signal. We observed that small belt displacements - likely to occur in clinical settings during patient handling - can induce errors of up to 30 mL on stroke volume estimation. In a second phase, we investigated the feasibility of a novel method for the non-invasive measurement of PAP by EIT. The method is based on the physiological relation linking the PAP to the velocity of propagation of the pressure waves in the pulmonary arteries. We hypothesized that the variations of this velocity, and therefore of the PAP, could be measured by EIT. In a bioimpedance model of the human thorax, we demonstrated the feasibility of our method in various types of pulmonary hypertensive disorders. Our EIT-derived parameter has shown to be particularly well-suited for predicting early changes in pulmonary hemodynamics due to its physiological link with arterial compliance. Finally, we validated experimentally our method in 14 subjects undergoing hypoxia-induced PAP changes. Significant correlation coefficients (range: [0.70, 0.98], average: 0.89) and small standard errors of the estimate (range: [0.9, 6.3] mmHg, average: 2.4 mmHg) were found between our EIT-derived systolic PAP and reference systolic PAP values obtained by Doppler echocardiography. In conclusion, there is a promising outlook for EIT in non-invasive hemodynamic monitoring. Our observations provide novel insights for the interpretation and understanding of EIT heart signals, and detail the physiological and metrological requirements for an accurate measurement of CO by EIT. Our novel PAP monitoring method, validated in vivo, allows a reliable tracking of PAP changes, thereby paving the way towards the development of a new branch of non-invasive hemodynamic monitors based on the use of EIT