显微外科手术条件下生理震颤的自回归建模
Autoregressive nodeling of physiological tremor under microsurgical conditions.
作者信息
Becker Brian C, Tummala Harsha, Riviere Cameron N
机构信息
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1948-51. doi: 10.1109/IEMBS.2008.4649569.
Tremor was recorded under simulated vitreoretinal microsurgical conditions as subjects attempted to hold an instrument motionless. Several autoregressive models (AR, ARMA, multivariate, and nonlinear) are generated to predict the next value of tremor. It is shown that a sixth order ARMA model predictor can predict a tremor having an amplitude of 96.6 +/- 84.5 microns RMS with an error of 8.2 +/- 5.9 microns RMS, a mean improvement of 47.5% over simple last-value prediction.
在模拟玻璃体视网膜显微手术条件下，当受试者试图保持器械静止不动时记录震颤情况。生成了几个自回归模型（AR、ARMA、多元和非线性模型）来预测震颤的下一个值。结果表明，一个六阶ARMA模型预测器能够预测幅度为96.6±84.5微米均方根值（RMS）的震颤，误差为8.2±5.9微米RMS，与简单的前值预测相比，平均改进了47.5%。
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