Closed-Loop Bladder Neuromodulation Therapy in Spinal Cord Injury Rat Model

Marlena N. Raczkowska, Wendy Y.X. Peh, Yuni Teh, Monzurul Alam, Shih Cheng Yen, Nitish V. Thakor

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Poor bladder management is a common and potentially life-threatening dysfunction among spinal cord injury (SCI) patients. In this condition, sensation from the bladder and voluntary control of micturition are lost, which might result in high post-void residual urine volume in the bladder, leading to renal impairment. Micturition can be driven using the sacral anterior root stimulator (SARS). However, commercially available SARS devices are not equipped with a closed-loop regulator for adaptive and automated control of bladder contractions. In our previous study, we developed a closed-loop control strategy for bladder emptying. In this paper we demonstrate the closed-loop neuromodulation feasibility in a SCI rat. The closed-loop strategy in this model achieved 71% voiding efficiency, higher than 40% efficiency obtained using open-loop stimulation. Our results provide a basis for developing an implantable closed-loop neural bladder prosthesis for SCI patients in the future.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages147-150
Number of pages4
ISBN (Electronic)9781538679210
DOIs
Publication statusPublished - 16 May 2019
Externally publishedYes
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: 20 Mar 201923 Mar 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Country/TerritoryUnited States
CitySan Francisco
Period20/03/1923/03/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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