{'fraction_non_alphanumeric': 0.04399472063352398, 'fraction_numerical': 0.05367355917289925, 'mean_word_length': 4.600985221674877, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Body composition modeling in the calf using an equivalent circuit model of multi-frequency bioimpedance analysis

An equivalent electrical circuit model is used to describe the response of different tissue components in the calf to multi-frequency current. This model includes seven electrical components: skin resistance, contact capacitance, fat resistance, fat capacitance, extracellular resistance, intracellular resistance and cell membrane capacitance. Calf bioimpedance was measured on 30 pts using a multi-frequency bioimpedance device (Xitron 4200) with a range of frequency from 5 kHz to 1000 kHz. MRI was performed on each measured calf to provide body composition components: fat, muscle mass and bone. An equivalent circuit containing seven parameters (P1, P2, P3, P4, Q1, Q2, Q3) was constructed to represent the model. To identify the effect of different body compositions on their parameters, subjects were subgrouped according to (1) their range of fat mass: F1 > 0.4 kg, F2 > 0.4 & F2 < 0.25 kg and F3 < 0.25 kg; (2) their range of muscle mass: M1 > 1.2 kg, M2 < 1.2 & M2 > 1.0 kg and M3 < 0.25 kg. Curve fitting and simulation programs (Matlab Toolbox) were used to obtain the solution of the electrical equations. The results show a decrease in impedance with an increase in excitation frequency that differed among subjects with different fat contents. Simulation results show a high correlation (R2 > 0.98) between the bioimpedance measurements and the value calculated from the model. There are significant differences in parameters P1 (32.5 ± 5.9 versus 26 ± 4.4, p < 0.05), P3 (−15 330 ± 3352 versus −10 973 ± 3448, p < 0.05) and P4 (42 640 versus 24 191, p < 0.05) between groups F1 and F3. P2 is significantly different (1045 ± 442 versus 1407 ± 349, p < 0.05) between groups M1 and M2. The parameters that characterize the bioimpedance data depend upon many more tissue characteristics of electrical properties than those incorporated in current models and they are affected by aspects of body composition that are not considered in the fitting of bioimpedance data. This study shows a new model and methodology to analyze bioimpedance data and further work is likely to lead to much better understanding of electrical properties of body tissue.