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In this paper, we present a digital signal processor (DSP) capable of monitoring the urinary bladder volume through afferent neural pathways. The DSP carries out real-time detection and can discriminate extracellular action potentials, also known as on-the-fly spike sorting. Next, the DSP performs a decoding method to estimate either three qualitative levels of fullness or the bladder volume value, depending on the selected output mode. The proposed DSP was tested using both realistic synthetic signals with a known ground-truth, and real signals from bladder afferent nerves recorded during acute experiments with animal models. The spike sorting processing circuit yielded an average accuracy of 92% using signals with highly correlated spike waveforms and low signal-to-noise ratios. The volume estimation circuits, tested with real signals, reproduced accuracies achieved by offline simulations in Matlab, i.e., 94% and 97% for quantitative and qualitative estimations, respectively. To assess feasibility, the DSP was deployed in the Actel FPGA Igloo AGL1000V2, which showed a power consumption of 0.5 mW and a latency of 2.1 ms at a 333 kHz core frequency. These performance results demonstrate that an implantable bladder sensor that perform the detection, discrimination and decoding of afferent neural activity is feasible.

Refractive urinary dysfunction in individuals suffering from neurogenic bladder syndrome can be treated with implanted neurostimulators that restore, to some degree, the control of the urinary bladder. A sensor capable of relaying feedback from bladder activity to the implanted neurostimulator is required to implement a closed-loop system to improve overall implant efficacy and minimize deleterious effects to neural tissue caused by continuous electrical stimulation. In this paper, we present a method that allows real-time estimation of bladder volume from the primary afferent activity of bladder mechanoreceptors. Our method was validated with data acquired from anesthetized rats in acute experiments. It was possible to qualitatively estimate three states of bladder fullness in 100% of trials when the recorded afferent activity exhibited a Spearman's correlation coefficient of 0.6 or better. Furthermore, we could quantitatively estimate bladder volume, and also its pressure, using timeframes of properly chosen duration. The mean volume estimation error was 5.8 ±3.1%. Our results also demonstrate that it is possible to quantify both phasic and tonic bladder responses during slow filling and isovolumetric measurements, respectively.

Chronic monitoring of bladder volume can improve the clinical diagnosis and the choice of therapeutic approach for patients suffering from urinary dysfunction. It can also be employed to notify patients or healthcare personnel when the bladder should be emptied. An early warning can be triggered either when functional bladder capacity is reached or when an abnormally high postvoiding residual volume remains in the bladder after an unfinished voiding. Currently, neuroprosthetic implants are used in the treatment of refractory patients with overactive bladder, with urgency-frequency or with voiding complications. These implants can further enhance their performance, and also reduce their adverse-effects, by implementing a conditional stimulation based on the ongoing information of bladder volume. In this paper, we review the measurement methods used in past studies, we analyze and assess them, and lastly we pinpoint the one that we consider the optimal one for chronic monitoring of bladder volume.