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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.
Yves Perriard, Yoan René Cyrille Civet, Stefania Maria Aliki Konstantinidi, Amine Benouhiba, Quentin Philippe Mario De Menech, Sloan Zammouri
Grégoire Courtine, Jocelyne Bloch, Jordan Squair
Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Stefania Maria Aliki Konstantinidi, Amine Benouhiba, Quentin Philippe Mario De Menech