Smart Bioimpedance Spectroscopy Device for Body Composition Estimation
David Naranjo-Hernández1, Javier Reina-Tosina1, Laura M Roa1
1Biomedical Engineering Group, University of Seville, 41092 Seville, Spain.
Sensors (Basel, Switzerland)
|December 28, 2019
Summary
This study introduces a smart bioimpedance spectroscopy device and a novel algorithm for accurate body composition estimation. The technology shows improved precision, especially in challenging conditions, outperforming existing methods.
Area of Science:
- Biomedical Engineering
- Medical Devices
- Physiological Measurement
Background:
- Bioimpedance spectroscopy (BIS) is a non-invasive technique used for estimating body composition.
- Accurate BIS measurements are crucial for clinical assessments and disease management.
- Existing BIS devices and algorithms face challenges with accuracy, particularly due to parasitic effects.
Purpose of the Study:
- To present a novel smart bioimpedance spectroscopy device for body composition estimation.
- To introduce an innovative algorithm for identifying Cole model parameters for enhanced accuracy.
- To validate the device and algorithm's performance in clinical settings.
Main Methods:
- Development of a multi-frequency bioimpedance spectroscopy device with wireless data transmission.
- Implementation of a new algorithm utilizing an extended Cole model with phase delay and three dispersions.
- Validation studies conducted on respiratory and peritoneal dialysis patients.
Main Results:
- The proposed device accurately measures bioimpedance modulus and phase across configurable frequencies.
- The novel algorithm demonstrated superior accuracy in Cole model parameter identification compared to existing methods.
- Validation studies confirmed the technology's feasibility and precision, outperforming a reference device in cases with parasitic effects.
Conclusions:
- The developed smart bioimpedance spectroscopy device and algorithm offer a robust and accurate solution for body composition estimation.
- The enhanced Cole model approach effectively mitigates parasitic effects, improving measurement reliability.
- This technology holds significant potential for clinical applications requiring precise body composition analysis.


