Supervised Learning Classifiers for Electrical Impedance-based Bladder State Detection

Urinary Incontinence afects over 200 million people worldwide, severely impacting the quality of life of individuals. Bladder state detection technology has the potential to improve the lives of people with urinary incontinence by alerting the user before voiding occurs. To this end, the objective of this study is to investigate the feasibility of using supervised machine learning classifers to determine the bladder state of full or not full from electrical impedance measurements. Electrical impedance data was obtained from computational models and a realistic experimental pelvic phantom. Multiple datasets with increasing complexity were formed for varying noise levels in simulation. 10-Fold testing was performed on each dataset to classify full and not full bladder states, including phantom measurement data. Support vector machines and k-Nearest-Neighbours classifers were compared in terms of accuracy, sensitivity, and specifcity. The minimum and maximum accuracies across all datasets were 73.16% and 100%, respectively. Factors that contributed most to misclassifcation were the noise level and bladder volumes near the threshold of full or not full. This paper represents the frst study to use machine learning for bladder state detection with electrical impedance measurements. The results show promise for impedance-based bladder state detection to support those living with urinary incontinence.