The development of human-like brain phantom is important for data acquisition in microwave imaging. The characteristics of the phantom should be based on the real human body dielectric properties such as relative permittivity. The development of phantom includes the greymatter and whitematter regions, each with a relative permittivity of 38 and 28 respectively at 10 GHz frequency. Results were compared with the value obtained from the standard library of Computer Simulation Technology (CST) simulation application and the existing research by Fernandez and Gabriel. Our experimental results show a positive outcome, in which the proposed mixture was adequate to represent real human brain for data acquisition.
Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.
This paper describes a model for predicting hemoglobin (Hb) by using bioelectrical impedance analysis (BIA) in dengue patients in the Hospital Universiti Kebangsaan Malaysia (HUKM). Bioelectrical impedance measurements were conducted on 83 (47 males and 36 females) serologically confirmed dengue fever (DF) and dengue hemorrhagic fever (DHF) patients during their hospitalization. The predictive equation for Hb was derived using multivariate analysis. We investigated all the parameters in BIA, patients' symptom and demographic data. In this developed model, four predictors (reactance (XC), sex, weight and vomiting) were found to be the best predictive factors for modeling Hb in dengue patients. However, the model can only explain approximately 42% of the variation in Hb status, thus single frequency bio-impedance stand-alone technique is insufficient to monitor Hb for the DF and DHF patients. Further investigation using multi-frequency BIA is recommended in modeling Hb to achieve the most parsimonious model.