APL Bioengineering (Dec 2023)

Decoding bladder state from pudendal intraneural signals in pigs

  • A. Giannotti,
  • S. Lo Vecchio,
  • S. Musco,
  • L. Pollina,
  • F. Vallone,
  • I. Strauss,
  • V. Paggi,
  • F. Bernini,
  • K. Gabisonia,
  • L. Carlucci,
  • C. Lenzi,
  • A. Pirone,
  • E. Giannessi,
  • V. Miragliotta,
  • S. Lacour,
  • G. Del Popolo,
  • S. Moccia,
  • S. Micera

DOI
https://doi.org/10.1063/5.0156484
Journal volume & issue
Vol. 7, no. 4
pp. 046101 – 046101-12

Abstract

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Neuroprosthetic devices used for the treatment of lower urinary tract dysfunction, such as incontinence or urinary retention, apply a pre-set continuous, open-loop stimulation paradigm, which can cause voiding dysfunctions due to neural adaptation. In the literature, conditional, closed-loop stimulation paradigms have been shown to increase bladder capacity and voiding efficacy compared to continuous stimulation. Current limitations to the implementation of the closed-loop stimulation paradigm include the lack of robust and real-time decoding strategies for the bladder fullness state. We recorded intraneural pudendal nerve signals in five anesthetized pigs. Three bladder-filling states, corresponding to empty, full, and micturition, were decoded using the Random Forest classifier. The decoding algorithm showed a mean balanced accuracy above 86.67% among the three classes for all five animals. Our approach could represent an important step toward the implementation of an adaptive real-time closed-loop stimulation protocol for pudendal nerve modulation, paving the way for the design of an assisted-as-needed neuroprosthesis.