State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China.
Comput Math Methods Med. 2012;2012:961967. doi: 10.1155/2012/961967. Epub 2012 Sep 10.
By introducing the conflicting effects of dynamic changes in blood flow, volume, and blood oxygenation, Balloon model provides a biomechanical compelling interpretation of the BOLD signal. In order to obtain optimal estimates for both the states and parameters involved in this model, a joint filtering (estimate) method has been widely used. However, it is flawed in several aspects (i) Correlation or interaction between the states and parameters is incorporated despite its nonexistence in biophysical reality. (ii) A joint representation for states and parameters necessarily means the large dimension of state space and will in turn lead to huge numerical cost in implementation. Given this knowledge, a dual filtering approach is proposed and demonstrated in this paper as a highly competent alternative, which can not only provide more reliable estimates, but also in a more efficient way. The two approaches in our discussion will be based on unscented Kalman filter, which has become the algorithm of choice in numerous nonlinear estimation and machine learning applications.
通过引入血流、容量和血氧动态变化的冲突影响，球囊模型为 BOLD 信号提供了一个引人入胜的生物力学解释。为了获得该模型中涉及的状态和参数的最优估计，联合滤波（估计）方法得到了广泛应用。然而，它在几个方面存在缺陷：（i）尽管在生物物理现实中不存在，但是状态和参数之间的相关性或相互作用被包含在内。（ii）状态和参数的联合表示必然意味着状态空间的维度很大，这反过来又会导致实现中的巨大数值成本。基于这一认识，本文提出并论证了一种双滤波方法作为一种极具竞争力的替代方法，该方法不仅可以提供更可靠的估计，而且还可以更有效地实现这一目标。我们讨论中的两种方法将基于无迹卡尔曼滤波，该滤波已成为许多非线性估计和机器学习应用中的首选算法。
