کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
557557 1451655 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison of the hemodynamic filtering methods and particle filter with extended Kalman filter approximated proposal function as an efficient hemodynamic state estimation method
ترجمه فارسی عنوان
مقایسه روشهای فیلترسازی همودینامیک و فیلتر ذرات با استفاده از فیلتر پیشنهاد شده کولمن فیلد تقریبی پیشنهاد شده به عنوان یک روش برآورد دقیق حالت همودینامیک
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

Estimating the hidden hemodynamic states that underlie measured blood oxygen level dependent (BOLD) signals is an important model inversion challenge in functional neuroimaging. Various filtering techniques are proposed in the literature. Those are Gaussian type approximated estimation techniques like Extended Kalman filter (EKF), Unscented Kalman filter (UKF), Cubature Kalman filter (CKF) as well as stochastic inference techniques like standard particle filters (PF) and auxiliary particle filter (APF). In this technical note, we compare particle filter type algorithms and Gaussian approximated inference methods. We also implement a particular type of particle filter that approximates the optimal proposal function by the Extended Kalman filter (PF-EKF). We show that the allegation that Extended Kalman type approximated methods are poor in performance is not true. On the contrary, they are better. We tested this assertion under different parameter sets, inputs, a wide range of noise conditions and unknown initial condition. This finding is important for developing fast and accurate alternative model inversion schemes, which is the topic of our subsequent paper.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 25, March 2016, Pages 99–107
نویسندگان
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