Improved Cole parameter extraction based on the least absolute deviation method

Yang, Yuxiang; Ni, Wenwen; Sun, Qiang; Wen, He; Teng, Zhaosheng

doi:10.1088/0967-3334/34/10/1239

Cited by 36 publications

(21 citation statements)

References 31 publications

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“…[34], where θ i denotes the angle between the horizontal axis and the radial direction at angular frequency ω i , and is calculated by the following equation:…”

Section: Simulated Datasetsmentioning

confidence: 99%

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Extraction of Cole parameters from the electrical bioimpedance spectrum using stochastic optimization algorithms

Gholami-Boroujeny

Bolić

2015

Med Biol Eng Comput

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Fitting the measured bioimpedance spectroscopy (BIS) data to the Cole model and then extracting the Cole parameters is a common practice in BIS applications. The extracted Cole parameters then can be analysed as descriptors of tissue electrical properties. To have a better evaluation of physiological or pathological properties of biological tissue, accurate extraction of Cole parameters is of great importance. This paper proposes an improved Cole parameter extraction based on bacterial foraging optimization (BFO) algorithm. We employed simulated datasets to test the performance of the BFO fitting method regarding parameter extraction accuracy and noise sensitivity, and we compared the results with those of a least squares (LS) fitting method. The BFO method showed better robustness to the noise and higher accuracy in terms of extracted parameters. In addition, we applied our method to experimental data where bioimpedance measurements were obtained from forearm in three different positions of the arm. The goal of the experiment was to explore how robust Cole parameters are in classifying position of the arm for different people, and measured at different times. The extracted Cole parameters obtained by LS and BFO methods were applied to different classifiers. Two other evolutionary algorithms, GA and PSO were also used for comparison purpose. We showed that when the classifiers are fed with the extracted feature sets by BFO fitting method, higher accuracy is obtained both when applying on training data and test data.

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“…[34], where θ i denotes the angle between the horizontal axis and the radial direction at angular frequency ω i , and is calculated by the following equation:…”

Section: Simulated Datasetsmentioning

confidence: 99%

“…the initial values and in case of noisy data, their premature convergence or convergence to local minima results in producing parameter sets that deteriorate the classification [29,34].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Cole parameters from the electrical bioimpedance spectrum using stochastic optimization algorithms

Gholami-Boroujeny

Bolić

2015

Med Biol Eng Comput

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“…Cole fitting method: Cole parameters are extracted for each spectrum of training dataset based on the method proposed in [27]. These parameters are fed to a LDA classifier in the case of simulated data and four types classifiers (linear, quadratic, decision tree and KNN) in the case of real measurements.…”

Section: Classification Taskmentioning

confidence: 99%

A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements

Nejadgholi

Bolić

2015

Computers in Biology and Medicine

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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%

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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Improved Cole parameter extraction based on the least absolute deviation method

Cited by 36 publications

References 31 publications

Extraction of Cole parameters from the electrical bioimpedance spectrum using stochastic optimization algorithms

Extraction of Cole parameters from the electrical bioimpedance spectrum using stochastic optimization algorithms

A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements

Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

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