Micera, SilvestroGiannotti A,Lo Vecchio S,Musco S.Pollina L.Vallone F.Strauss I.Paggi, Valentina MarieBernini F.Gabisonia K.Carlucci L.Lenzi C.Pirone A.Giannessi E.Miragliotta V.Lacour, StéphanieDel Popolo G.MocciaS.2023-10-232023-10-232023-10-232023-10-0110.1063/5.0156484https://infoscience.epfl.ch/handle/20.500.14299/201883Neuroprosthetic 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.Machine learningSignal processingDiseases and conditionsNeural engineeringNeuroprostheticsAnimal modeltext::journal::journal article::research article