NDLI: Estimating bladder pressure from sacral dorsal root ganglia recordings

Content Provider	IEEE Xplore Digital Library
Author	Bruns, T.M. Gaunt, R.A. Weber, D.J.
Copyright Year	2011
Description	Author affiliation: Departments of Physical Medicine and Rehabilitation and Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 USA (Weber, D.J.) \|\| Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA (Bruns, T.M.; Gaunt, R.A.)
Abstract	Individuals with dysfunctional bladders may benefit from devices that track the bladder state. Recordings from pelvic and sacral nerve cuffs can detect bladder contractions, however they often have low signal quality and are susceptible to interference from non-bladder signals. Microelectrode recordings from sacral dorsal root ganglia (DRG) neurons may provide an alternate source for obtaining high quality sensory signals for bladder pressure monitoring. In this study, penetrating microelectrode arrays were inserted in the S1 and S2 DRG in two cats to record afferent spiking activity at different bladder pressures. Multivariate linear regression models were used to estimate bladder pressure from the spiking activity of DRG neurons. The best estimates were obtained with populations of 5–10 units primarily from the S2 DRG, with root mean square errors of 3.0–3.2 cm H2O (correlation coefficients of 0.5–0.9). This work demonstrates the feasibility of monitoring bladder pressure from DRG recordings.
Starting Page	4239
Ending Page	4242
File Size	979244
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091052
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bladder Water Firing Monitoring Microelectrodes Neurons Training
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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