Predicting urinary bladder voiding by means of a linear discriminant analysis: Validation in rats

Abstract

AimsThe objective of this work is to investigate whether changes in bladder pressure’s patterns can be used to forecast voiding events in rats with both normal and overactive detrusor. MethodsA voiding forecasting algorithm based on machine learning was developed. Raw pressure curves as well as their corresponding power bands were used as inputs to a linear discriminant analysis classifier. Performance was evaluated on held-out test data and was statistically validated via comparison to random predictors. ResultsUsing the band-power feature, 93% and 99% of the alarms were respectively generated within 95 s before voiding for normal and hyperactive bladder conditions respectively. The same algorithm was assessed using the band-power feature. It showed performances achieving respective success rates of 99% and 97% for normal and hyperactive bladder condition respectively with alarms generated within 45 s before voiding. ConclusionsWe have demonstrated the feasibility of detecting the pre-voiding periods in rats with normal and overactive bladders with a high success rate. SignificanceTo our knowledge, this is the first study that demonstrates the possibility of predicting voiding in rats with a machine learning algorithm based on a Linear Discriminant Analysis. Our work was compared to other relevant studies and showed better results. With this study, accurate urinary bladder voiding forecasting could be implemented in closed-loop advisory/intervention devices.