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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.

Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients' comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared. Index Terms-Chronic obstructive pulmonary diseases (COPD), COVID-19, breathing monitoring techniques, Doppler radar, ultra-wideband (UWB) pulse radar. I. INTRODUCTIONT TE main function of the respiratory system is gas exchange. Oxygen is transferred from the external ambient into our bloodstream, while carbon dioxide is expelled outside [1]. Fig. 1 illustrates the respiratory system including the upper and lower respiratory tract regions. When inhaling, the air flow passes through the larynx and the trachea, and then splits into two bronchi. Each bronchus is divided into two Manuscript

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.

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