Evaluation of Decoding Algorithms for Estimating Bladder Pressure from Dorsal Root Ganglia Neural Recordings.
Evaluation of Decoding Algorithms for Estimating Bladder Pressure from Dorsal Root Ganglia Neural Recordings.
复制标题
从背根神经节神经记录估计膀胱压力的解码算法评估。
DOI：
10.1007/s10439-017-1966-6
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发表时间：
2018
影响因子：
3.8
通讯作者：
Bruns,TimM
中科院分区：
文献类型：
--
作者：
Ross,ShaniE;Ouyang,Zhonghua;Rajagopalan,Sai;Bruns,TimM
与当前的开环刺激装置相比，用于膀胱控制的闭环装置可以提供更大的临床益处。先前的研究已经证明了使用骶水平背根神经节（DRG）的单单位记录解码膀胱压力的可行性。与简单的多单元阈值活动相比，用于区分单个单元的自动在线排序可能计算量大且不可靠。在这项研究中，使用DRG多单位记录解码膀胱压力的可行性进行了研究。广泛的特征选择方法和三种算法（多元线性回归，基本卡尔曼滤波器，和非线性自回归移动平均模型）被用来创建训练模型，并提供验证适合膀胱压力的数据收集在七个麻醉猫实验。基于标准化均方根误差NRMSE（17 ± 7%），具有正则化的非线性自回归移动平均（NARMA）模型提供了最准确的膀胱压力估计。基本卡尔曼滤波器产生与膀胱压力的最高相似性，平均相关系数CC为0.81 ± 0.13。最好的算法集（基于NRMSE）进一步评估从慢性猫实验获得的数据。测试结果产生了10.7%和0.61的NRMSE和CC，分别来自于2周前记录的数据训练的模型。根据离线分析，在用于检测膀胱收缩的闭环方案中实现NARMA将提供鲁棒的控制信号。在膀胱神经假体的闭环算法的最终集成将需要随着时间的推移的参数和信号稳定性的评价。
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.