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Body composition assessment by bioelectrical impedance analysis and body mass index in individuals with chronic spinal cord injury

34 63.4 1.74 20.95 Tetraplegic active SD 8.31 9.61 0.03 2.89 CI 23.69- 51.47- 1.7-1.78 17.35- 44.31 75.33 24.54 Mean 37.25 76.98 1.78 24.11 Tetraplegic inactive SD 9.94 10.46 0.05 2.82 CI 30.94- 70.33- 1.75- 22.32- 43.56 83.62 1.82 25.89 Abbreviations: SD, standard deviation; CI, confidence intervals; BMI, body mass index In Figure 1 , the difference between groups in fat percentage and BMI can be observed. For able-bodied persons, fat

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Electrical impedance tomography methods for miniaturised 3D systems

. H. Tizzard A. Gibson A. P. a. T Tidswell Sparkes M. K. Dehghani H. Binnie C. D. Holder D. S. Neuroimage 2003 20 752 764 org/10.1016/S1053-8119(03)00301-X 17 B. Amm, T. Kao, X. Wang, G. Boverman, J. Sabatini, J. Ashe, J. Newell, S. Member, D. Isaacson and S. Member, IEEE Eng. Med. Biol. Soc., 2014, 6064–6067. Amm B. Kao T. Wang X. Boverman G. Sabatini J. Ashe J. Newell J. Member S. Isaacson D. Member S. IEEE Eng. Med. Biol. Soc 2014 6064 6067 18 H. Reinius, J. B. Borges, F

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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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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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Simulation of impedance measurements at human forearm within 1 kHz to 2 MHz

.ejcn.1601384 Pietrobelli A Nu-ez C Zingaretti G Battistini N Morini P Wang ZM Yasumura S Heymsfield SB Assessment by bioimpedance of forearm cell mass: a new approach to calibration Eur J Clin Nutr 2002 56 8 723 8 dx.doi.org/10.1038/sj.ejcn.1601384 14 Ohmine Y, Morimoto T, Kinouchi Y, Iritani T, Takeuchi M, Haku M, Nishitani H. Basic study of new diagnostic modality according to non-invasive measurement of the electrical conductivity of tissues. J Med Invest. 2004;51(3-4):218–25. dx.doi.org/10.2152/jmi.51.218 10.2152/jmi.51

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Monitoring thoracic fluid content using bioelectrical impedance spectroscopy and Cole modeling

Cole model and on geometrical properties of the impedance arc. Indicator dilution measurements obtained through cardiac magnetic resonance imaging were used as a reference for the changes in pulmonary fluid volume. Eight healthy subjects were included in the study. The Cole model parameters of the study group at baseline were: R 0 = 51.4 ± 6.7 Ω, R ∞ = 25.0 ± 7.0 Ω, f c = 49.0 ± 10.5 kHz, α = 0.687 ± 0.027, the resistances of individual fluid compartments were R E = 51.4 ± 6.7 Ω, R I = 50.5 ± 22.9 Ω, the fluid distribution ratio was K = 1.1 ± 0.3, and the

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Enhancing sharp features by locally relaxing regularization for reconstructed images in electrical impedance tomography

. Guardo R. 1996 Electrical impedance tomography: regularized imaging and contrast detection IEEE Transactions on Medical Imaging 15 2 170 – 179 https://doi.org/10.1109/42.491418 Adler, A. and Lionheart, W.R., 2006. Uses and abuses of EIDORS: an extensible software base for EIT. Physiological Measurement, 27(5), pp.S25-42. https://doi.org/10.1088/0967-3334/27/5/s03 10.1088/0967-3334/27/5/S03 16636416 Adler A. Lionheart W.R. 2006 Uses and abuses of EIDORS: an extensible software base for EIT Physiological Measurement 27 5 S25 – 42

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Classification of different erythrocyte cells by using bioimpedance surface acoustic wave and their sedimentation rates

liquids will vary. The time rates of changing for these heights are related to the sedimentation velocity, where higher velocity means high rates of these lengths or heights. The sedimentation velocity of the RBCs as function of their radii and the volume fractions is given by [13]. (5) v = ρ R B C − ρ p l a s ∗ g ∗ d 2 / 18 μ 1 + 2.5 Φ $$v=\left( {{\rho }_{RBC}}-{{\rho }_{plas}} \right)*g*\,\,{{{d}^{2}}}/{18\mu \left( 1

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Spatial resolution in electrical impedance tomography: A topical review

. Meas vol. 27 no. 5 S25 S42 2006 doi.org/10.1088/0967-3334/27/5/S03 59 F. S. Lee, Optimum array processing, vol. 35, no. July. John Wiley and Sons, 2008. Lee F. S. Optimum array processing vol. 35 no. July John Wiley and Sons 2008 60 S. Manohar, A. Kharine, J. C. G. van Hespen, W. Steenbergen, and T. G. van Leeuwen, "The Twente Photoacoustic Mammoscope: system overview and performance.," Phys. Med. Biol., vol. 50, no. 11, pp. 2543–57, 2005. https://doi.org/10.1088/0031-9155/50/11/007 10.1088/0031-9155/50/11/007 Manohar S. Kharine A

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