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The design, fabrication, and performance of pre-screening tool using bioelectrical tissue profile is described. A probe of 8 electrodes using active-probe-sensing (AP-sensing) module was linearity distortion in the frequency response bonded to the 8 sub-regions of the breast surface forming the Bioimpedance transfer measurement system. Each measurement channel acquired the data from 1/8 of breast according to each breast defined in 8 sub-regions with split current injection channel and response behavioural bioelectricity detection. For this tissue bioelectrical measurement system, the electrodes were placed on the breast surface and before the bioelectrical profile were measured, we investigated a closed loop technique to compensate for the effects and measure the channel-skin-contact impedance. The AP-sensing module and connecting pads could be placed in the measurement electrode-Bras according to different breast size, and all measurement sub-regions should be equivalent for each case, but could be different in scale of 1- 2cm in related to different breast size. Bilateral structure was applied to compare each breast tissue bioelectrical properties in related to tissue behavioural in different frequency. Bioelectrical measurement efficiency was evaluated by the use of bioelectrical plots equivalent to a theoretical plot of the pure tissue profile versus average intracellular and extracellular and admittance behavioural of breast tissue able to flow electric field using Cole-Cole structure tissue modelling as a well-known bioelectrical tissue profile. The bioelectrical tissue profile as a pre-screening tool using theoretical pure tissue profiles and experimental measurements were evaluated in related a conventional Bioimpedance spectroscopy instruments. Breast bioelectrical profile of different breast density categories and average measurement values were significant, according to exist of fibro-glandular tissues in the total breast volume. The different of the theoretical values corresponding to pure fatty and fibro-glandular breast tissue behaviour was slightly different with the experimental measurements. We demonstrated the bioelectrical profile of breast tissue and extracting bioelectrical features that comparing in a bilateral structure to apply bioelectrical features as a supplementary data in the machine learning algorithms and present correspond risk factor for susceptibility of breast cancer in future studies. The preliminary equivalent theoretical and experimental results, evaluate the possibility of this new, non-imaging and label-free quantitative technique.
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