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.
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.
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.
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.
A mechanistic mathematical model for electrical impedance spectroscopy (EIS) measurements of human skin is analyzed, leading to a reduced model and approximate solutions. In essence, the model considers a complex-valued Laplace equation in the frequency domain for the alternating current from a circular EIS probe passing through the layers – stratum corneum, viable skin and adipose tissue – of human skin in the frequency range 1 kHz – 1 MHz. The reduced model, which only needs to be solved numerically for the viable skin with modified boundary conditions, is verified with the full set of equations (non-reduced model): good agreement is found with a maximum relative error of less than 3%. A Hankel transform of the reduced model allows for approximate solutions of not only the measured impedance but also the point-wise potential distribution in the skin. In addition, the dimensionless numbers governing the EIS are elucidated and discussed.
In this study the transformed theory is applied to derive the dielectric characteristics of cells, considering the electrorotation (ER) peak frequency. In current studies, estimations of low frequency, which are credible for the values less than 1 mS/m for medium conductivity, are used to obtain the corresponding permittivity and conductivity of cells. Unlike the presented works, the transformed theory applies the comprehensive statement for corresponding permittivity and conductivity of cells. In the transformed theory, the membrane and interior characteristics could be obtained from the high and the low frequencies of peak ER, for all values of conductivity of medium. Characteristics of cells are obtained via optimization of an equation for the conductivity of medium regarding the peak ER frequency. The optimization process is performed applying genetic algorithm due to its swift adaptation to the problem and faster convergence.
Current guidelines do not recommend bioelectrical impedance analysis (BIA) in patients with implanted cardiac devices. There is no data on the influence of such devices over the parameters assessed by BIA. We aimed to assess the influence of cardiac devices on the parameters assessed by BIA as well as to evaluate the likelihood of electromagnetic interference of BIA in patients with implanted cardiac devices. Sixty-two consecutive patients over 18 years of age who underwent single (PM) or multisite (CRT) pacemaker or defibrillator (ICD) implantation were included. Body composition assessment was done using a single frequency device, on both right and left sides, before and after cardiac device implantation. During BIA analysis after device implantation, we did real-time telemetry to assess electromagnetic interference. Patients were 67+14 years old and 51.6% male. PM was implanted in 52 patients (83.9%), ICD in 7 (11.3%), ICD with CRT in 2 (3.2%) and CRT in 1 (1.6%). During real-time telemetry, there was no electromagnetic interference including interruption of telemetry. Default device programming did not change after BIA assessment. After surgery, resistance and fat mass were smaller, while cellular mass, fat-free mass, metabolic rate and total body water/ body weight increased, on right and left sides measurements. We concluded that decreased resistance and related parameters after device implantation were probably influenced to a change in hydration status, regardless of the implanted device. Bioimpedance analysis is safe in patients with an implanted cardiac device.
Purpose: To assess body composition and obesity in individuals with spinal cord injury (SCI) who practice and do not practice physical activity using body mass index (BMI) and bioelectrical impedance analysis (BIA). Methods: 39 patients with SCI went through BIA evaluation and BMI was assessed. Patients were divided into four groups according to injury level (paraplegia or tetraplegia) and physical activity achievement (active or inactive). Results: 22 individuals with paraplegia (7 active and 15 inactive) and 17 with tetraplegia (5 active and 12 inactive) were evaluated. BMI, fat percentage, fat mass, lean tissue mass, total body water (TBW), and TBW percentage were assessed in groups. Tetraplegic inactive groups showed higher fat percentage featuring obesity. For paraplegic active group mean fat percentage was 19.61% (±9.27) and mean fat mass was 16.66 kg (±9.71) and for paraplegic inactive group fat percentage was 23.27% (±5.94) and fat mass 18.59 kg (±7.58). For tetraplegic groups in active group the fat percentage was 17.14% (±6.32) and fat mass was 11.22 kg (±5.16) and for inactive group mean fat percentage was 33.68% (±4.74) and fat mass was 25.59 kg (±2.91). When paraplegic and tetraplegic inactive groups were compared differences were observed in fat percentage (p = 0.0003) and fat mass (p = 0.0084). Also, when tetraplegic groups (activeXinactive) were compared differences in percentage (p = 0.0019) and fat mass (p = 0.034) were observed. Only for the paraplegic inactive group BMI result was higher than 25 kg/m2. Conclusion: BMI does not discriminate between obesity levels in individuals with SCI and physical activity can improve body composition and prevent obesity in SCI patients.