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.