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

Thomas NiederhauserElena S GafnerTarcisi CantieniMichelle GrämigerAndreas HaeberlinDominik ObristFiona BurkhardFrancesco Clavica

Published in: Neurourology and urodynamics (2017)

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.

Keyphrases

end stage renal disease
machine learning
ejection fraction
deep learning
spinal cord injury
newly diagnosed
chronic kidney disease
high resolution
neural network
electronic health record
big data
quantum dots