Publication: Estimation of Urine Volume and Urine Conductivity Using Electrical Bioimpedance Based on the Neural Network Method
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Issued Date
2024-06-01
Resource Type
ISSN
23455829
eISSN
23455837
Scopus ID
2-s2.0-85198292789
Journal Title
Frontiers in Biomedical Technologies
Volume
11
Issue
3
Start Page
423
End Page
432
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Biomedical Technologies Vol.11 No.3 (2024) , 423-432
Suggested Citation
Ouypornkochagorn T., Chiangchin P., Ngamdi N., Limpisophon T., Dowloy A. Estimation of Urine Volume and Urine Conductivity Using Electrical Bioimpedance Based on the Neural Network Method. Frontiers in Biomedical Technologies Vol.11 No.3 (2024) , 423-432. 432. doi:10.18502/fbt.v11i3.15885 Retrieved from: https://hdl.handle.net/20.500.14740/20138
Corresponding Author(s)
Other Contributor(s)
Abstract
Purpose: Urine volume and urine conductivity monitoring allow better care for urinary tract infection disease. Urine volume and conductivity involve electrical bioimpedance change at the lower abdomen. In previous studies, bioimpedance has been only used for estimating the volume, and the estimation error significantly increases when the conductivity changes. Materials and Methods: In this work, the neuron network technique is proposed to determine both the volume and the conductivity based on the measured bioimpedance data on a sixteen-electrode configuration. Nine architectures of neuron networks were investigated by simulation. Eleven body models were created, consisting of muscle, fat, pelvis bone, rectum, and bladder. Seven bladder sizes, eleven conductivities, and eight levels of Signal-to-Noise Ratio (SNRs) were simulated. Results: The result showed that the neural network method could efficiently estimate with an average of 1.04% volume error and 2.85% conductivity error. The performance remained stable with a signal-to-noise ratio higher than 60 dB, but it may reduce 2-8 times at lower SNRs. The moderate fat content provided high performance. The performance would be worsened if the bladder size was very small and the conductivity was low. The performance was increased when the volume was moderate, i.e. 302 ml, and the conductivity was higher than 1.76 S/m. The 3-layer architecture with 1024, 512, and 2 neurons yielded the highest performance. The 2-layer architecture with hidden neurons higher than 512 provided a comparative performance with only 0.9-1.5% lesser performance. Conclusion: Neural network technique can be used to estimate urine volume and urine conductivity with excellent performance.