Detection and quantification of overactive bladder activity in patients: Can we make it better and automatic?

Niederhauser, Thomas; Stojkova Gafner, Elena; Cantieni, Tarcisi; Grämiger, Michelle; Haeberlin, Andreas; Obrist, Dominik; Burkhard, Fiona C.; Clavica, Francesco (2017). Detection and quantification of overactive bladder activity in patients: Can we make it better and automatic? Neurourology and urodynamics, 37(2), pp. 823-831. Wiley-Liss 10.1002/nau.23357

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AIMS:
To explore the use of time-frequency analysis as an analytical tool to automatically detect pattern changes in bladder pressure recordings of patients with overactive bladder (OAB). To provide quantitative data on the bladder's non-voiding activity which could improve the current diagnosis and potentially the treatment of OAB.
METHODS:
We developed an algorithm, based on time-frequency analysis, to analyze bladder pressure during the filling phase of urodynamic studies. The algorithm was used to generate a bladder overactivity index (BOI) for a quantitative estimation of the average bladder non-voiding-activity. We tested the algorithm with one control group and two groups of patients with OAB symptoms: one group with detrusor overactivity (DO), assessed by an experienced urologist (OAB-with-DO group), and another group for which detrusor overactivity was not diagnosed (OAB-without-DO group).
RESULTS:
The algorithm identified diagnostically significant data on the bladder non-voiding activity in a specified frequency range. BOI was significantly higher for both OAB groups compared to the control group: the median value of BOI was twice as big in OAB-without-DO and more than four times higher in OAB-with-DO compared to control group. Moreover the algorithm was successfully tested to detect episodes of detrusor overactivity.
CONCLUSIONS:
We have shown that a simple algorithm, based on time-frequency analysis of bladder pressure, may be a promising tool in the clinical setting. The algorithm can provide quantitative data on non-voiding bladder activity in patients and quantify the changes according to phenotype. Moreover the algorithm can detect DO, showing potential for triggering conditional bladder stimulation.

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