| Title |
Evaluation of Decoding Algorithms for Estimating Bladder Pressure from Dorsal Root Ganglia Neural Recordings
|
|---|---|
| Published in |
Annals of Biomedical Engineering, November 2017
|
| DOI | 10.1007/s10439-017-1966-6 |
| Pubmed ID | |
| Authors |
Shani E. Ross, Zhonghua Ouyang, Sai Rajagopalan, Tim M. Bruns |
| Abstract |
A closed-loop device for bladder control may offer greater clinical benefit compared to current open-loop stimulation devices. Previous studies have demonstrated the feasibility of using single-unit recordings from sacral-level dorsal root ganglia (DRG) for decoding bladder pressure. Automatic online sorting, to differentiate single units, can be computationally heavy and unreliable, in contrast to simple multi-unit thresholded activity. In this study, the feasibility of using DRG multi-unit recordings to decode bladder pressure was examined. A broad range of feature selection methods and three algorithms (multivariate linear regression, basic Kalman filter, and a nonlinear autoregressive moving average model) were used to create training models and provide validation fits to bladder pressure for data collected in seven anesthetized feline experiments. A non-linear autoregressive moving average (NARMA) model with regularization provided the most accurate bladder pressure estimate, based on normalized root-mean-squared error, NRMSE, (17 ± 7%). A basic Kalman filter yielded the highest similarity to the bladder pressure with an average correlation coefficient, CC, of 0.81 ± 0.13. The best algorithm set (based on NRMSE) was further evaluated on data obtained from a chronic feline experiment. Testing results yielded a NRMSE and CC of 10.7% and 0.61, respectively from a model that was trained on data recorded 2 weeks prior. From offline analysis, implementation of NARMA in a closed-loop scheme for detecting bladder contractions would provide a robust control signal. Ultimate integration of closed-loop algorithms in bladder neuroprostheses will require evaluations of parameter and signal stability over time. |
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| United States | 2 | 40% |
| Canada | 1 | 20% |
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|---|---|---|
| Members of the public | 3 | 60% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
| Scientists | 1 | 20% |
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|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
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|---|---|---|
| Student > Ph. D. Student | 8 | 21% |
| Researcher | 7 | 18% |
| Other | 2 | 5% |
| Student > Bachelor | 2 | 5% |
| Student > Postgraduate | 2 | 5% |
| Other | 2 | 5% |
| Unknown | 16 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
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| Medicine and Dentistry | 3 | 8% |
| Agricultural and Biological Sciences | 2 | 5% |
| Physics and Astronomy | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 14 | 36% |