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A DSP for sensing the bladder volume through afferent neural pathways.

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    This study introduces a digital signal processor (DSP) for real-time bladder volume monitoring using neural signals. The DSP achieves high accuracy in detecting and decoding bladder fullness, demonstrating feasibility for implantable sensors.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • Urinary bladder volume monitoring is crucial for managing various medical conditions.
    • Current methods often lack real-time, continuous feedback.
    • Neural pathways offer a potential avenue for advanced bladder monitoring systems.

    Purpose of the Study:

    • To develop and validate a digital signal processor (DSP) for real-time urinary bladder volume monitoring.
    • To enable detection, discrimination, and decoding of afferent neural activity related to bladder fullness.
    • To assess the feasibility of an implantable sensor for bladder management.

    Main Methods:

    • Real-time signal processing using a custom digital signal processor (DSP).
    • On-the-fly spike sorting for discriminating extracellular action potentials.
    • Decoding algorithms for estimating bladder fullness (qualitative or quantitative).
    • Validation with synthetic and real neural signals from animal models.

    Main Results:

    • Spike sorting accuracy of 92% with challenging signal conditions.
    • Volume estimation accuracy of 94% (quantitative) and 97% (qualitative) with real neural data.
    • Low power consumption (0.5 mW) and latency (2.1 ms) demonstrated on an FPGA.
    • Successful deployment and testing of the DSP system.

    Conclusions:

    • The developed DSP effectively monitors urinary bladder volume via afferent neural pathways.
    • High accuracy in spike sorting and volume estimation supports the system's efficacy.
    • Low power and latency confirm the feasibility of an implantable bladder sensor.