Decoding bladder state from pudendal intraneural signals in pigs

• Published in: APL bioengineering 2023-12, Vol.7 (4), p.046101-046101
• Main Authors: Giannotti, A., Lo Vecchio, S., Musco, S., Pollina, L., Vallone, F., Strauss, I., Paggi, V., Bernini, F., Gabisonia, K., Carlucci, L., Lenzi, C., Pirone, A., Giannessi, E., Miragliotta, V., Lacour, S., Del Popolo, G., Moccia, S., Micera, S.
• Format: Article
• Language: English
• ISSN: 2473-2877; 2473-2877
• DOI: 10.1063/5.0156484

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.