On the accuracy of sequence methods for baroreflex sensitivity estimation
AbstractIn the absence of a true gold standard for non-invasive baroreflex sensitivity estimation, it is difficult to quantify the accuracy of the variety of techniques used. A popular family of methods, usually entitled ‘sequence methods’ involves the extraction of (apparently) correlated sequences from blood pressure and RR-interval data and the subsequent fitting of a regression line to the data. This paper discusses the accuracy of sequence methods from a system identification perspective, using both data ge nerated from a known mathematical model and spontaneous baroreflex data. It is shown that sequence methods c...
Source: Australasian Physical and Engineering Sciences in Medicine - April 2, 2024 Category: Biomedical Engineering Source Type: research
A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images
Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening us... (Source: BioMedical Engineering OnLine)
Source: BioMedical Engineering OnLine - April 2, 2024 Category: Biomedical Engineering Authors: Laifa Yan, Shan Ling, Rongsong Mao, Haoran Xi and Fei Wang Tags: Research Source Type: research
Spatial Registration of Heterogeneous Sensors on Mobile Platforms
Accurate georegistration is required in multi-sensor data fusion, since even minor biases in spatial registration can result in large errors in the converted target geolocation. This paper addresses the problem of estimating and correcting sensor biases in target geolocation. Aiming to solve the spatial registration problem in the case where heterogeneous measurements are provided by mobile sensor (active or passive) platforms, this paper proposes a moving heterogeneous sensor registration (MDSR) algorithm based on maximum likelihood estimation. The MDSR algorithm decouples the offset biases from the attitude biases and up...
Source: IEEE Transactions on Signal Processing - April 1, 2024 Category: Biomedical Engineering Source Type: research
Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM
Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vect...
Source: IEEE Transactions on Signal Processing - April 1, 2024 Category: Biomedical Engineering Source Type: research
IEEE Transactions on Biomedical Circuits and Systems Publication Information
null (Source: IEEE Transactions on Biomedical Circuits and Systems)
Source: IEEE Transactions on Biomedical Circuits and Systems - April 1, 2024 Category: Biomedical Engineering Source Type: research
