@inproceedings{dfcd6d820c6f48a894e3851ce67cdddd,
title = "Linear Regression for Estimating Bladder Volume with Voltage Signals",
abstract = "Urinary incontinence is a common condition that can severely impact the lives of those who have it. Bladder volume monitoring solutions that exploit the electrical differences of different tissues in the pelvis have the potential to help medical personnel in the decision-making process with urinary incontinence. In this work, we investigate linear regression as a means of assigning bladder volume to the measured voltage values. We found that linear regression outperforms the previously studied machine learning regression algorithms by nearly a factor of 4. This linear regression approach is also more effectively able to handle volumes outside the training boundaries in comparison to previous work in the field. More work is needed to further improve the estimate of bladder volume based on the voltage signals, especially at high noise levels.",
keywords = "Bladder volume monitoring, COST EMF-MED, Electrical impedance, Machine learning, Regression, Voltage",
author = "Eoghan Dunne and Adam Santorelli and Brian McGinley and Martin Orhalloran and Emily Porter",
note = "Publisher Copyright: {\textcopyright} 2018 FESB, University of Split.; 1st EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields, EMF-Med 2018 ; Conference date: 10-09-2018 Through 13-09-2018",
year = "2018",
month = nov,
day = "6",
doi = "10.23919/EMF-MED.2018.8526019",
language = "English",
series = "EMF-Med 2018 - 1st EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields and COST EMF-MED Final Event with 6th MCM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "EMF-Med 2018 - 1st EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields and COST EMF-MED Final Event with 6th MCM",
}