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Quantitative evaluation of blood glucose concentration using impedance sensing devices

Biosens. Bioelectron 2003 19 3 209 – 217 14 Tura A, Sbrignadello S, Barison S, Conti S, Pacini G. Dielectric properties of water and blood samples with glucose at different concentrations. IFMBE Proc. 2007;16:194-197. 10.1007/978-3-540-73044-6_48 Tura A Sbrignadello S Barison S Conti S Pacini G Dielectric properties of water and blood samples with glucose at different concentrations IFMBE Proc 2007 16 194 – 197

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Impedimetric characterization of human blood using three-electrode based ECIS devices

subsequent packaging and measurements. Finally, the individual device was fixed within a printed circuit board and electrical connections were taken from the device to external equipment using thin metal wires. Cloning cylinders were then aligned and attached to serve as an electrolyte reservoir around the three-electrode system by using PDMS as glue. Sample preparation Phosphate buffered saline (PBS) was prepared by mixing 8 g of NaCl, 0.2 g of KC1, 1.15 g Na 2 HPO 4 , and 0.2 g of KH 2 PO 4 in one litre distilled water at pH 7.4 [ 25 ]. To prepare the serum, the

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Diagnosis of mitral insufficiency using impedance cardiography technique ICG

-cardiography, especially the Doppler echocardiography [ 25 , 26 , 27 ]. However, this technique is expensive and often not necessary for making a diagnosis [ 28 ]. Otherwise, there are few studies, which discussed the ability of the impedance cardiography method to diagnose Mitral Insufficiency. Karnegis et al . have calculated an index from the ICG tracings, which may be useful in identifying patients with mitral regurgitation [ 29 ]. Schieken et al . have determined a mitral regurgitation fraction that is the ratio of the areas of the first systolic and diastolic waves [ 30

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

presenting and analyzing bioimpedance data. The advantage in machine learning methods is the possibility of learning generalizable predictive patterns in combining variables in a non-linear fashion, possibly increasing the predictive performance compared to simpler models. In addition, machine learning can be used to perform automatic feature extraction, useful when there is a lot of variables (e.g. different immittance parameters over many frequencies) and the important ones are not known. In some cases, such as clinical monitoring, the prediction performance is

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An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

analyzed, each containing 30 min of annotated ECG recordings of continuous ECG. The ECG recordings in this database contain the normal clinical recordings, complex ventricular, junctional, and supraventricular arrhythmias [ 25 ]. These records were sampled at 360 Hz and band pass filtered at 0.1 – 100 Hz [ 25 ]. In this paper, our method was evaluated by five classes of beats including: non-ectopic beats (N), fusion beats (F), supraventricularectopic beats (S), ventricular ectopic beats (V), and unknown beats (U). The summarization of the five classes of ECG beat samples

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Biomass measurement of living Lumbriculus variegatus with impedance spectroscopy

Introduction Electrical properties of biological tissues have been studied for over a century [ 1 ]. A large variety of different biological tissues have been investigated with the help of impedance spectroscopy, a detailed review on various human and animal tissue and blood samples can be found, e.g., in the review by Gabriel et al. [ 2 ]. Fricke and Curtis [ 3 ] investigated the impedance of yeast cell suspensions in the early 1930´s. They concluded that the impedance at the surface of the yeast cell is derived from a poorly conducting membrane which acts

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Modelling the Ability of Rheoencephalography to Measure Cerebral Blood Flow

New Perspectives in Rheoencephalography Medical Information Science Reference 2008 p 990 – 995 3 Moskalenko YU, Weinstein G, Masalov I, Halvorson P, et al. Multifrequency REG: Fundamental Background,Informational Meaning and Ways of Data Analysis and Automation. American Journal of Biomedical Engineering. 2012;2(4):163–174. 10.5923/j.ajbe.20120204.03 Moskalenko YU Weinstein G Masalov I Halvorson P et al Multifrequency REG: Fundamental

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Investigating the quasi-oscillatory behavior of electrical parameters with the concentration of D-glucose in aqueous solution

)\overrightarrow{E} \\ & \,\,\,\,\,\,\,\frac{\varepsilon }{{{\varepsilon }_{0}}}=\frac{n{{p}^{2}}}{3k{{T}_{{{\varepsilon }_{0}}}}}+1 \\ \end{align}$$ The solution of DI water and glucose will have three different types of interactions at the molecular level: the water-water, glucose-glucose and water-glucose dipoles that finally determine the overall dielectric behavior of the solution [ 25 , 26 ]. Therefore, the net polarization of an elementary volume, contributed from these three different components, can be written as (10) p 2 = ( p W cos   θ W ) 2 + ( p G cos   θ G ) 2 + 2 p W p G

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Cole Parameter Estimation from the Modulus of the Electrical Bioimpeadance for Assessment of Body Composition. A Full Spectroscopy Approach

TBW ECF ICF FM Subject 1 -0.04 / -0.04 0.12 / 0.04 -0.12 / -0.09 0.06 / 0.06 Subject 2 -0.71 / -0.42 0.87 / 0.05 -0.87 / -0.48 0.97 / 0.59 Subject 3 -0.13 / -0.12 0.19 / 0.03 -0.19 / -0.15 0.18 / 0.17 Subject 4 -0.41 / -0.35 0.59 / 0.12 -0.59 / -0.46 0.57 / 0.47 Subject 5 -0.25 / -0.18 0.49 / 0.05 -0.49/ -0.24 0.34 / 0.25 Average -0.31 / -0.22 0.45 / 0.06 -0.45 / -0.28 0.42 / 0.31 Note: The percentage of ECF and ICF are referred to the TBW and the FM is expressed in

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Assessing cardiac preload by the Initial Systolic Time Interval obtained from impedance cardiography

;115:19-28 14706465 10.1016/S1388-2457(03)00312-2 Burgess HJ Penev PD Schneider R Van Cauter E Estimating cardiac autonomic activity during sleep: impedance cardiography, spectral analysis, and Poincaré plots Clin Neurophysiol 2004 115 19 28 4 Schweiger E, Wittling W, Genzel S, Block A. Relationship between sympathovagal tone and personality traits. Person Individ Diff 1998;25:327-37 10.1016/S0191-8869(98)00093-2 Schweiger E Wittling W Genzel S Block A Relationship between sympathovagal tone and personality traits Person Individ Diff

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