A DSP for Sensing the Bladder Volume Through Afferent Neural Pathways
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it