Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings
Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings
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DOI：
10.1109/tbme.2012.2234456
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发表时间：
2013-05-01
影响因子：
4.6
通讯作者：
Jutten, Christian
中科院分区：
文献类型：
--
作者：
Niknazar, Mohammad;Rivet, Bertrand;Jutten, Christian
在本文中，我们提出了一种扩展的非线性贝叶斯过滤框架，用于从单通道提取心电图（ECG），就像从腹部传感器提取胎儿心电图时遇到的情况一样。记录的信号被建模为多个心电图的总和。它们中的每一个都由非线性动态模型来描述，该模型先前是为了生成高度真实的合成心电图而提出的。因此，每个心电图在该模型中都有相应的项，因此即使波在时间上重叠，也可以有效地区分。对胎儿和母体心电图之间的噪声水平、幅度和心率比率的不同值进行的参数敏感性分析显示了其对于大量这些参数值的有效性。该框架还通过从实际腹部记录以及实际双胞胎心磁图提取胎儿心电图进行了验证。
In this paper, we present an extended nonlinear Bayesian filtering framework for extracting electrocardiograms (ECGs) from a single channel as encountered in the fetal ECG extraction from abdominal sensor. The recorded signals are modeled as the summation of several ECGs. Each of them is described by a nonlinear dynamic model, previously presented for the generation of a highly realistic synthetic ECG. Consequently, each ECG has a corresponding term in this model and can thus be efficiently discriminated even if the waves overlap in time. The parameter sensitivity analysis for different values of noise level, amplitude, and heart rate ratios between fetal and maternal ECGs shows its effectiveness for a large set of values of these parameters. This framework is also validated on the extractions of fetal ECG from actual abdominal recordings, as well as of actual twin magnetocardiograms.