Oliver Pabst, Steinar Andersen, Soban Ali Bhatti, Jørgen Brevik, Simen Anthony Fallaas, Mads Fjeldstad, Artiom Gubaidulin, Kjetil Vermundsen Madsen, Mats Ricardo Nomedal, Sondre Fortun Slettemoen, Halvard Yri Adriaenssens, Sean Andre Hansen, Tommy Myrvik, Eivind Rostad, Torleif Skår, Kristian Tuv, Sebastian Edmund Pedersen Wood and Daniel Åsen
Non-linear electrical properties of a (biological) tissue can be revealed by non-linear electrical measurements, which means that the applied stimulus itself affects the measurement. If resulting voltage–current plots exhibit pinched hysteresis loops, the underlying tissue may be classified as a memristor, a state dependent resistor. The aloe vera plant and apples have been found to be memristors. However, polarization processes on the electrodes are also non-linear and may affect the measurement. Apples and aloe vera conduct electrical current very well and it is likely that the recordings are actually dominated by the polarization impedance of the electrodes. Here, we study the non-linear properties of aloe vera and apples with two different measurement electrode types. Furthermore, we measured also on the extracted liquids from one aloe vera leaf and one apple, leading to similar results. We concluded, unlike previous studies on these subjects, that the memristive properties originate from electrochemical reactions on the electrodes rather than the apples or aloe vera themselves.
In December of 2018 I published my consolidated findings of a closed-form description of propagated signaling phenomena in the membrane of an axon . Those results demonstrate how intracellular conductance, the thermodynamics of magnetization, and current modulation, function together in generating an action potential in a unified differential equation. At present, I report on a subsequent finding within this model. Namely, evidence of quantized magnetic flux Φ0 in an axon.
Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.
Abdul Hamid Ismail, Georg Schlieper, Marian Walter, Jürgen Floege and Steffen Leonhardt
The feasibility of bioimpedance spectroscopy (BIS) techniques for monitoring intradialytic changes in body fluids is advancing. The aim of this study was to compare the knee-to-knee (kkBIS) with the traditional whole-body (whBIS) with respect to continuous assessment of fluid volume status in hemodialysis patients. Twenty patients divided into two groups, hemodynamically stable and unstable, were recruited. Bioimpedance data from two different electrodes configurations (hand-to-foot and knee-to-knee) were collected and retrospectively analysed. A good correlation between the two methods with respect to changes in extracellular resistance (Re) and Re normalized for ultrafiltration volume (ΔRe/UFV) with p < 0.001 was observed. The relationship between relative change (%) in ΔRe and that in patient weight was most notable with kkBIS (4.82 ± 3.31 %/kg) in comparison to whBIS (3.69 ± 2.90 %/kg) in unstable patients. Furthermore, results based on kkBIS showed a reduced ability of the thigh compartments to keep up with the volume changes in the trunk for unstable patients. kkBIS provided a comparable sensitivity to whBIS even in patients at risk of intradialytic hypotension while avoiding the need for the complex implementation imposed by whBIS or other configurations.
Mohammad Karimi Moridani, Fatemeh Choopani and Mandana Kia
The purpose of this paper is to identify differences between abnormal and normal lung signals gathered by an EIT device, which is a new, non-invasive system that seeks the electrical conductivity and permittivity inside a body. Lung performances in patients are investigated using Phase Space Mapping technique on Electrical EIT signals. The database used in this paper contains 82 registered records of 52 individuals with proper lung volume. The results of this paper show that as the delay parameter (τ) increases, the SD1 parameter of phase space mapping indicates a significant difference between normal and abnormal lung volumes. The value of the SD1 parameter with τ = 6 in the case that the lung volume is in a normal condition is 342.57 ± 32.75 while it is 156.71 ± 26.01 in non-optimal mode. This method can be used to identify the patients’ lung volumes with chronic respiratory illnesses and is an accurate assessment of the diverse methods to treat respiratory system illnesses in addition to saving various therapeutic costs and dangerous consequences that are likely to occur by using improper treatment methods. It can also reduce the required treatment durations.
Leslie D. Montgomery, Richard W. Montgomery, Wayne A. Gerth, Michael Bodo, Julian M. Stewart and Marty Loughry
This paper describes a new combined impedance plethysmographic (IPG) and electrical bioimpedance spectroscopic (BIS) instrument and software that will allow noninvasive real-time measurement of segmental blood flow, intracellular, interstitial, and intravascular volume changes during various fluid management procedures. The impedance device can be operated either as a fixed frequency IPG for the quantification of segmental blood flow and hemodynamics or as a multi-frequency BIS for the recording of intracellular and extracellular resistances at 40 discrete input frequencies. The extracellular volume is then deconvoluted to obtain its intravascular and interstitial component volumes as functions of elapsed time. The purpose of this paper is to describe this instrumentation and to demonstrate the information that can be obtained by using it to monitor segmental compartment volume responses of a pig model during simulated hemorrhage and resuscitation. Such information may prove valuable in the diagnosis and management of rapid changes in the body fluid balance and various clinical treatments.
B. Tsai, H. Xue, E. Birgersson, S. Ollmar and U. Birgersson
We determine the in-vivo dielectric properties—resistivity and relative permittivity—of living epidermis and dermis of human skin soaked with a physiological saline solution for one minute between 1 kHz and 1 MHz. This is done by fitting approximate analytical solutions of a mechanistic model for the transport of charges in these layers to a training set comprising impedance measurements at two depth settings on stripped skin on the volar forearm of 24 young subjects. Here, the depth settings are obtained by varying the voltage at a second inject on the electrical-impedance-spectroscopy probe. The model and the dielectric properties are validated with a test set for a third depth setting with overall good agreement. In addition, the means and standard deviations of the thicknesses of living epidermis and dermis are estimated from a literature review as 61±7 μm and 1.0±0.2 mm respectively. Furthermore, extensions to resolve the skin layers in more detail are suggested.
Image reconstruction in EIT is an inverse problem, which is ill posed and hence needs regularization. Regularization brings stability to reconstructed EIT image with respect to noise in the measured data. But this is at the cost of smoothening of sharp edges and high curvature details of shapes in the image, affecting the quality. We propose a novel iterative regularization method based on detection of probable location of the inclusion, for locally relaxing the regularization by appropriate amount, to overcome this problem. Local relaxation around inclusion allows reconstruction of its high curvature shape details or sharp features at the same time giving benefits of higher regularization in remaining areas of the image. The proposed method called DeTER is implemented using a small plug-in to EIDORS (Electrical Impedance and Diffused Optical Reconstruction Software) in a MATLAB environment. Parameters like CNR, correlation coefficients of shape descriptor functions and relative size of reconstructed targets have been computed to evaluate the effectiveness of the technique. The performance of DeTER is tested and verified on simulated data added with Gaussian noise for inclusions of different shapes. Both conducting and nonconducting inclusions are considered. The method is validated using open EIT data shared by ‘Finnish inverse problem society’ and also by reconstructing image of internal void of a papaya fruit from the data acquired by an EIT system developed in our laboratory. The reconstructed images corresponding to the open EIT data clearly show the shapes similar to original objects, with sharp edges and curvature details. The shapes obtained in the papaya image are shown to correspond to the actual void using shape descriptor function. The results demonstrate that the proposed method enhances the sharp features in the reconstructed image with few iterations without causing geometric distortions like smoothening or rounding of the edges.
The first issue of the Journal of Electrical Bioimpedance saw the light in 2010 by the personal initiative of two men from the University of Oslo, Prof. Sverre Grimnes and Prof. Ørjan G. Martinsen, who has been the editor-in-chief of our Journal during all these ten years. With the sense of gratitude, we hope that he continues his persistent work also during the approaching next decade in the new conditions with a growing number of bioimpedance publications worldwide. However, every success creates new problems, some of which are discussed below.
The relation between a biological process and the changes in passive electrical properties of the tissue is often non-linear, in which developing prediction models based on bioimpedance spectra is not trivial. Relevant information on tissue status may also lie in characteristic developments in the bioimpedance spectra over time, often neglected by conventional methods. The aim of this study was to explore possibilities in machine learning methods for time series of bioimpedance spectra, where we used organ ischemia as an example. Based on published data on the development of the bioimpedance spectrum during liver ischemia, a simulation model was made and used to generate sets of synthetic data with different levels of organ-to-organ variation, measurement noise and drift. Three types of artificial neural networks were employed in learning to predict the ischemic duration, based on the simulated datasets. The simulated prediction performance was very dependent on the amount of training examples, the organ-to-organ variation and the selection of input variables from the bioimpedance spectrum. The performance was also affected by noise and drift in the measurement, but a recurrent neural network with long short-term memory units could obtain good predictions even on noisy and drifting measurements. This approach may be relevant for further exploration on several applications of bioimpedance having the purpose of predicting a biological state based on spectra measured over time.