Validation of an efficient and continuous urodynamic monitoring system for awake, unrestrained, chronic rodent studies.

The postvoid residual (PVR) is an important measure of bladder function, but obtaining PVR is burdensome because bladder volume must be measured at the time of voiding. The PVR measurement problem has led to experimental tricks in animal studies (infusing the bladder at supraphysiological rates and limiting animal observation windows) to keep the number of observed voids statistically robust while reducing the time an experimenter must be present. Our solution to the PVR measurement problem is a system called Automatic Monitoring for Efficient, Awake, Sensitive, Urine Residual Estimation (AMEASURE). AMEASURE combines metabolic cages and optimization algorithms to estimate continuously PVR for every voiding event 24 h/day for multiple weeks, without artificial bladder infusion, continuous experimenter supervision, anesthesia, or restraints. Using AMEASURE, we obtained voided volumes, PVRs, and other urodynamic parameters continuously for 21 days in 10 healthy female Sprague-Dawley rats. Importantly, this required only one manual measurement of animals' bladder volume every 12 h. We validated the accuracy of the system experimentally and in simulation. We detected marked differences in voiding frequency and efficiency between light and dark cycles and found that voiding frequency increased over time during the dark cycle (but not the light cycle), due to surgical recovery, cage acclimation, and socialization. This tool enhances the relevance of rodent models to the study of human lower urinary tract by expanding observation periods and obviating the need to infuse the bladder and facilitates the study of conditions for which behavioral, social, or circadian factors play essential roles.

Neural network based forward prediction of bladder pressure using pudendal nerve electrical activity.

Individuals with spinal cord injury or neurological disorders have problems in urinary bladder storage and in voiding function. In these people, the detrusor of bladder contracts at low volume and this causes incontinence. The goal of bladder control is to increase the bladder capacity by electrical stimulation of relative nerves such as pelvic nerves, sacral nerve roots or pudendal nerves. For this purpose, the bladder pressure has to be monitored continuously. In this paper, we propose a method for real-time estimating the bladder pressure using artificial neural network. The method is based upon measurements of electroneurogram (ENG) signal of pudendal nerve. This approach yields synthetic bladder pressure estimates during bladder contraction. The experiments were conducted on three rats. The results show that neural predictor can provide accurate estimation and prediction of bladder pressure with good generalization ability. The average error of 1-second and 5-second ahead prediction of bladder pressure are 9.62% and 10.54%, respectively.