Decoding bladder state from pudendal intraneural signals in pigs

Abstract: 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

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
APL bioengineering. - 7, 4 (2023) , 046101, ISSN: 2473-2877

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2024
Urheber
Giannotti, Alice
Lo Vecchio, Sara
Musco, Stefania
Pollina, Leonardo
Vallone, Fabio
Strauss, Ivo
Paggi, Valentina
Bernini, Fabio
Gabisonia, Khatia
Carlucci, Lucia
Lenzi, C.
Pirone, Andrea
Giannessi, Elisabetta
Miragliotta, Vincenzo
Lacour, Stéphanie
Del Popolo, Giulio
Moccia, Sara
Micera, Silvestro

DOI
10.1063/5.0156484
URN
urn:nbn:de:bsz:25-freidok-2543503
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:25 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Beteiligte

  • Giannotti, Alice
  • Lo Vecchio, Sara
  • Musco, Stefania
  • Pollina, Leonardo
  • Vallone, Fabio
  • Strauss, Ivo
  • Paggi, Valentina
  • Bernini, Fabio
  • Gabisonia, Khatia
  • Carlucci, Lucia
  • Lenzi, C.
  • Pirone, Andrea
  • Giannessi, Elisabetta
  • Miragliotta, Vincenzo
  • Lacour, Stéphanie
  • Del Popolo, Giulio
  • Moccia, Sara
  • Micera, Silvestro
  • Universität

Entstanden

  • 2024

Ähnliche Objekte (12)