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The effect of heart pulsatile on the measurement of artery bioimpedance

Introduction Bioimpedance is a widely used technique to measure the body composition due to its various advantages such as noninvasiveness, accuracy, applicability and low cost [ 1 ]. The measurement of artery bioimpedance is proposed in this study because it is used to diagnose numerous of blood diseases such as the cholesterol level, the foundation of stenosis, and diabetes [ 1 ]. Furthermore, the studying of the heart pulsatile effect may be used to measure the heart rate as a novel method for heart rate detection based on bioimpedance phenomena. The heart

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Applications of bioimpedance measurement techniques in tissue engineering

the tissue engineered constructs. However, these methods such as histology staining, are destructive and time-consuming and require fixing and cutting the tissue cultures ( 6 ). Therefore, there is a need for real-time and noninvasive monitoring techniques to evaluate the quality of the tissue engineered constructs before implanting them in the body, without the need to use fluorescents or radioactive labels or destructive methods. This in addition, would reduce the number of animals required for this purpose ( 5 , 6 ). Bioimpedance Measurements Basics of

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Knee-to-knee bioimpedance measurements to monitor changes in extracellular fluid in haemodynamic-unstable patients during dialysis

in systolic blood pressure. IDH increases patient morbidity (e.g. via nausea, vomiting, headaches and muscle cramps), reduces treatment efficacy, and increases the risk of mortality [ 2 ]. In recent years, multi-frequency bioimpedance spectroscopy (BIS) techniques have become more clinically attractive as they enable non-invasive, simple, and relatively inexpensive tools to assess continuously the fluid status during HD [ 3 ]. A high level of accuracy for whole-body BIS (whBIS) compared with gold standards (e.g. dilution methods) have been reported in literature

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Statistical methods for bioimpedance analysis

1 Introduction When doing measurements, statistics are needed if we want to describe the data (descriptive statistics) or if we want to draw conclusions based on the data (inferential statistics). There is a vast amount of statistical methods in the literature, and the choice of method depends on what we want to know and what type of data we have. In this paper we give an overview of the most basic and the most relevant methods for bioimpedance analysis along with examples within the bioimpedance field. Because bioimpedance measurements often are done as

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Possibilities in the application of machine learning on bioimpedance time-series

Introduction Analysis of the passive electrical properties of tissue (bioimpedance) can be challenging as the data is complex, the data amount can be large, methods of interpretation are vast, and the electrical properties often have a non-linear relation to the biological property of interest. Raw immittance data from bioimpedance measurements are typically presented as admittance, impedance or dielectric parameters, represented by real and imaginary components. As the electrical properties of tissue always are frequency dependent (1), bioimpedance will

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Monitoring Change of Body Fluid during Physical Exercise using Bioimpedance Spectroscopy and Finite Element Simulations

[ 6 ]. To avoid these consequences, the monitoring of body composition is important. Bioimpedance spectroscopy (BIS) allows such monitoring in a non-invasive, rapid and inexpensive way. Compared with other measurement methods, BIS provides a good estimation of water content during changes in hydration status [ 6 , 7 ]. However, to obtain precise BIS measurements controlled conditions are required to avoid the influence of external factors such as electrode placement, subject’s posture and/or body movements [ 9 ]. In relation to physical exercise, several other

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Low power current sources for bioimpedance measurements: a comparison between Howland and OTA-based CMOS circuits

Introduction Multifrequency electrical bioimpedance, also called Electrical Impedance Spectroscopy (EIS), has been widely used as a non-invasive technique for measuring many passive electrical properties from biological materials, such as: cancerous tissues ( 1 , 2 , 3 , 4 ); tumors ( 5 , 6 ), meningitis ( 7 ) and brain cellular oedema ( 8 , 9 ). It can also be used for analyzing body composition ( 10 , 11 ) and bovine milk quality ( 12 , 13 ). Also, it is considered fast, inexpensive, practical, and efficient ( 14 , 15 ). Its availability for emerging

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On the selection of excitation signals for the fast spectroscopy of electrical bioimpedance

frequency components in it, decreases as well. In electrical bioimpedance (EBI) measurements, unfortunately, the SNR cannot be improved by increasing the overall amplitude of the summary excitation signal, since it is limited to low values because of both two issues: a) satisfying the criteria of linearity of the LTI systems and b) fulfilling the security needs for living tissues [ 3 ], [ 4 ]. Even for non-biologic measurements, the allowable input signal range and power supply voltage of electronic components, both limit the allowed amplitude of signals. Moreover, due

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Textrode-enabled transthoracic electrical bioimpedance measurements – towards wearable applications of impedance cardiography

Introduction The implementation and use of textile technology and garments for electrical bioimpedance (EBI) measurements have been studied before, with reported results that are consistent and encouraging. Nonetheless, applied research regarding measurement performance of the garments is still necessary. Previous studies of bio potential sensing [ 1 , 2 ] and EBI for body composition analysis have shown the feasibility of using, textile electrodes, typically called “textrodes” [ 3 , 4 ]. In impedance cardiography (ICG) the use of a measurements garment

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Geometric parameters optimization of planar interdigitated electrodes for bioimpedance spectroscopy

-off frequency 1.6·10 4 and 1.7·10 4 Hz respectively. Fig. 13a Behavior of simulated electrical Bioimpedance for the blood medium by varying the cell constant of the sensor with changing the number of electrodes N, while leaving the same contact area of sensor 1000 × 1000 microns. Fig. 13b Behavior of corresponding electrical phase by varying the number of electrodes N. As we mentioned in the paragraph of Optimizing the electrodes geometry , the optimal number of fingers N has a minimum for N = 2 which is the lowest possible number of fingers

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