Efficient Computational Techniques in Bioimpedance Spectroscopy

Paterno, Aleksander S.; Negri, Lucas Hermann; Bertemes-Filho, Pedro

doi:10.5772/36307

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…In general, the raw impedance data are fitted in a model. The fitting process can be done using different techniques, such as Levenberg–Marquardt (LM) [ 13 ], particle swarm optimization (PSO) [ 14 ], genetic algorithm (GA) [ 15 ], and bacterial foraging optimization (BFO) [ 16 ]. However, the fitting process does not remove noise and parasitic effects from raw data, and hence an electrical model should represent the data including all those effects.…”

Section: Introductionmentioning

confidence: 99%

Extracting parasite effects of electrical bioimpedance measurements

Dutra

Bertemes-Filho

2018

Journal of Electrical Bioimpedance

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The objective of this work is to develop a technique for filtering parasitic effects from the impedance spectra (IS) measured in biological material phantoms. IS data are contaminated with unexpected capacitive and inductive effects from cable, input/output amplifiers capacitances, electrode polarization, temperature and contact pressure when collecting data. It is proposed a model which contains an RLC-network in series with the Cole model (RSC), then called RLC-Cole. It was built four circuits composed by resistors, capacitors and inductors. An impedance analyzer (HF2IS) was used to perform the measurements in the frequency range of 1 to 3000 kHz. Data were fitted into the model and comparisons to the nominal values were made. In order to validate the proposed model, a gelatin phantom and a chicken breast muscle impedance spectra were also collected and analyzed. After filtering, Cole fitting was performed. Results showed a maximum root-mean-square error of 1% for the circuits, 2.63% for the gelatin phantom, whereas 2.01% for the chicken breast. The RLC-Cole model could significantly remove parasitic effects out of a tissue impedance spectrum measured by a 4-point electrode probe. This may be highly important in EIS systems whose objective is to discriminate a normal tissue from a cancerous one.

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Section: Introductionmentioning

confidence: 99%

Extracting parasite effects of electrical bioimpedance measurements

Dutra

Bertemes-Filho

2018

Journal of Electrical Bioimpedance

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“…Multifrequency electrical bioimpedance, also called Electrical Impedance Spectroscopy (EIS), has been widely used as a non-invasive technique for measuring many passive electrical properties from biological materials, such as: cancerous tissues (1)(2)(3)(4); tumors (5,6), meningitis (7) and brain cellular oedema (8,9). It can also be used for analyzing body composition (10,11) and bovine milk quality (12,13). Also, it is considered fast, inexpensive, practical, and efficient (14,15).…”

Section: Introductionmentioning

confidence: 99%

Low power current sources for bioimpedance measurements: a comparison between Howland and OTA-based CMOS circuits

Bertemes-Filho

Vincence

Santos

et al. 2012

Journal of Electrical Bioimpedance

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Electrical Bioimpedance Analysis has been widely used as a non-invasive technique for characterizing tissues. Most systems use a wideband and a high precision instrumentation, specially the current source. The objective of this work is to compare the Howland circuit with three OTA-based floating voltage controlled current sources. The results show that both Current Conveyor and class-AB OTA have a wider output current frequency response and both output impedance is 226 % bigger than the Howland circuit at 1 MHz. The class-AB OTA circuit presents a power consumption of 4.6 mW whereas 1.6 mW for the Current Conveyor. The results might be useful for cell impedance measurements and for very low power bioimpedance applications

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“…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].…”

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confidence: 99%

“…Bioimpedance model identification is normally obtained by means of nonlinear Least Squares (NLLS) methods, which aim at obtaining the best coefficients for the Cole model that fits the curve minimizing the squared sum of the error between the measured data and the modeled values [23,25,27,28]. Other methods employ a stochastic resolution approach like the particle-swarm optimization algorithm [29,30], bacterial foraging optimization method [30] or genetic algorithms [30,31]. These methods do not often solve analytically the problem of parameter identification, but use complex algorithms of successive approximations that can only be executed off-line on personal computers.However, the biggest problem in the parameter identification process is the occurrence of some kind of perturbation, noise or parasitic effect.…”

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Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

Naranjo-Hernández

Reina‐Tosina

Roa

et al. 2019

Sensors

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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...

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Efficient Computational Techniques in Bioimpedance Spectroscopy

Cited by 12 publications

References 34 publications

Extracting parasite effects of electrical bioimpedance measurements

Extracting parasite effects of electrical bioimpedance measurements

Low power current sources for bioimpedance measurements: a comparison between Howland and OTA-based CMOS circuits

Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

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