Frequency-domain reconstruction of signals in electrical bioimpedance spectroscopy

Paterno, Aleksander S.; Stiz, R. A.; Bertemes-Filho, Pedro

doi:10.1007/s11517-009-0533-1

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…However, these results of single bioimpedance measurements is limited to the extracellular cell and can be enhanced and improved by using bioimpedance spectroscopy techniques [23,24,33] where measurement can be obtained in the intracellular cell at different frequencies ranges.…”

Section: Discussionmentioning

confidence: 99%

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

Ibrahim

Faisal

Salim

et al. 2010

Med Biol Eng Comput

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This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.

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Section: Discussionmentioning

confidence: 99%

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

Ibrahim

Faisal

Salim

et al. 2010

Med Biol Eng Comput

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“…For the use of the phase/modulus retrieval algorithm in Z Cole (s) the independent term corresponding to the resistance, R ∞ , causes the frequency dependent function to satisfy neither the phase-nor the modulus-retrieval algorithm conditions (Hayes et al, 1980;Paterno et al, 2009). In other words, the experimental points to be used with the phase/modulus retrieval algorithm must be previously tested with known bioimpedance spectrum data to verify if the process is applicable.…”

Section: Cole-cole Fractional Order Impedance Functionmentioning

confidence: 99%

Efficient Computational Techniques in Bioimpedance Spectroscopy

Paterno¹,

Negri²,

Bertemes-Filho³

2012

Applied Biological Engineering - Principles and Practice

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“…Model-based simulations could be the key to predict these ''invisible'' changes in body fluid and thereby avoid errors due to fluid redistributions. Even so the present model needs R e values as input, impedance values at specific frequencies could be used [18,19,33].…”

Section: Consideration Of Individual Differencesmentioning

confidence: 99%

Model-based correction of the influence of body position on continuous segmental and hand-to-foot bioimpedance measurements

Medrano

Eitner

Walter

et al. 2010

Med Biol Eng Comput

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Bioimpedance spectroscopy (BIS) is suitable for continuous monitoring of body water content. The combination of body posture and time is a well-known source of error, which limits the accuracy and therapeutic validity of BIS measurements. This study evaluates a model-based correction as a possible solution. For this purpose, an 11-cylinder model representing body impedance distribution is used. Each cylinder contains a nonlinear two-pool model to describe fluid redistribution due to changing body position and its influence on segmental and hand-to-foot (HF) bioimpedance measurements. A model-based correction of segmental (thigh) and HF measurements (Xitron Hydra 4200) in nine healthy human subjects (following a sequence of 7 min supine, 20 min standing, 40 min supine) has been evaluated. The model-based compensation algorithm represents a compromise between accuracy and simplicity, and reduces the influence of changes in body position on the measured extracellular resistance and extracellular fluid by up to 75 and 70%, respectively.

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Frequency-domain reconstruction of signals in electrical bioimpedance spectroscopy

Cited by 10 publications

References 21 publications

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

Efficient Computational Techniques in Bioimpedance Spectroscopy

Model-based correction of the influence of body position on continuous segmental and hand-to-foot bioimpedance measurements

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