Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

Abstract

The purpose of this work is to describe a first approach to a smart bioimpedance spectroscopy device for its application to the estimation of body composition. The proposed device is capable of carrying out bioimpedance measurements in multiple configurable frequencies, processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies, and transmitting the processed information wirelessly. Another novelty of this work is a new algorithm for the identification of Cole model parameters, which is the basis of body composition estimation through bioimpedance spectroscopy analysis. Against other proposals, the main advantages of the proposed method are its robustness against parasitic effects by employing an extended version of Cole model with phase delay and three dispersions, its simplicity and low computational load. The results obtained in a validation study with respiratory patients show the accuracy and feasibility of the proposed technology for bioimpedance measurements. The precision and validity of the algorithm was also proven in a validation study with peritoneal dialysis patients. The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device.Sensors 2020, 20, 70 2 of 27 during pregnancy and lactation [6], in the evaluation of the risk of various pathologies [7,8], as a marker or direct cause of disease [9,10], during the process of decision making in an illness [11], aging [12] or rehabilitation processes [13], as a complement to the diagnosis and follow-up of conditions related to the cardiovascular system [14,15], in oncology [16] and even in sports science [17], among others.Despite the advances in the clinical application of bioimpedance, there are still some challenges to be solved, such as the integration of devices into e-Health systems supporting remote user monitoring [18]. In addition, the complexity of bioimpedance systems is usually quite high (current injection, voltage measurement, demodulation, processing, etc.) and the use of high-frequency signals (tens to hundreds of kHz) generally requires high power consumption, so new challenges arise for the optimization of hardware in size, energy efficiency, robustness and precision [1,19]. On the other hand, although the bioimpedance spectroscopy technique has proven to be more precise and robust than the single frequency technique, most devices base their operation on the measurement on a single frequency [1,20].Other issues are related to the models and algorithms used to estimate the BC from bioimpedance measures. A key point for an adequate BC estimation by bioimpedance analysis is a correct parameter identification of the bioimpedance model, usually the Cole model [1,[21][22][23][24][25][26][27][28][29][30][31]. Sometimes, this parameterization is performed without directly measuring the impedance values [21][22][23]. Other authors use...

Physical Validation of a Residual Impedance Rejection Method during Ultra-Low Frequency Bio-Impedance Spectral Measurements

“…One area of potential use appears to be high-precision full-body measurements [ 10 , 17 ], bio-impedance measurements in different parts of the body [ 40 ], and multi-sensory applications, such as non-invasive blood glucose measurements [ 41 ]. An additional area of promising application may be represented by measurements on cell lines [ 14 , 42 ].…”

“…This makes it significantly more difficult to test biological systems, as the data can only be corrected “offline”, after measurements have been carried out by special mathematical procedures, using errors from different measurements. In our article, we aim to solve this problem with the help of a new method that can detect parasitic capacities, residual impedances, generator anomalies, and so on, in parallel with data collection [ 17 ]. In the course of data collection, the system’s ground point is connected by the biological sample, where the data are collected in relation to the ground point of the system (i.e., potential measurement).…”

“…When , it represents the standard Debye model [ 22 ]. Therefore, the spectrum of a tissue may be more appropriately described in terms of multiple Cole–Cole dispersions, which can be used in conjunction with a choice of parameters (appropriate to each tissue) to predict the dielectric behaviour over the desired frequency range [ 17 , 23 ].…”

Special Issue “Body Sensors Networks for E-Health Applications”

“…Some of the works published in the Special Issue have been related to biomedical sensing technologies. In Reference [ 1 ], the design and validation of a new bioimpedance sensor device for the estimation of body composition is presented. Bioimpedance technology has many practical advantages that favor its use in biomedical sensors—non-invasive, repeatable measurements as many times as desired, provide information about the internal physiological processes of the subject under study.…”

“…The clinical usefulness of bioimpedance for the estimation of the user’s nutritional and hydration status has been demonstrated in numerous studies related to nutrition, chronic diseases, pregnancy and lactation, oncology, monitoring of the elderly, evaluation of athletes, and so forth. The device proposed in Reference [ 1 ] addresses some of the challenges of bioimpedance technology related to the precision of the measurements, the use of high-frequency signals (close to MHz) and the energy efficiency. Contrary to the standard measurement technique that uses a single frequency, the proposed device is based on bioimpedance spectroscopy, a more robust and precise technique.…”