Predicting urinary bladder voiding by means of a linear discriminant analysis: Validation in rats
| Autor: | E. van Asselt, A. Tantin, Sami Hached, E. Bou Assi, Mohamad Sawan |
|---|---|
| Přispěvatelé: | Urology |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Urinary bladder business.industry 0206 medical engineering Urology Health Informatics 02 engineering and technology Linear discriminant analysis classifier urologic and male genital diseases Linear discriminant analysis 020601 biomedical engineering Bladder pressure female genital diseases and pregnancy complications 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Feature (computer vision) Signal Processing Medicine Hyperactive bladder business Overactive detrusor 030217 neurology & neurosurgery |
| Zdroj: | Biomedical Signal Processing and Control, 55:101667. Elsevier |
| ISSN: | 1746-8094 |
| DOI: | 10.1016/j.bspc.2019.101667 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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