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

A. Tantin*, E. Bou Assi, E. van Asselt, S. Hached, M. Sawan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)


Aims: The 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. Methods: A 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. Results: Using 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. Conclusions: We have demonstrated the feasibility of detecting the pre-voiding periods in rats with normal and overactive bladders with a high success rate. Significance: To 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.

Original languageEnglish
Article number101667
JournalBiomedical Signal Processing and Control
Publication statusPublished - Jan 2020

Bibliographical note

Funding Information:
Authors acknowledge the financial support from NSERC of Canada .

Publisher Copyright:
© 2019 Elsevier Ltd


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