Mantini D, Alleva G, Comani S
Department of Informatics and Automation Engineering, Marche Polytechnic University, Ancona, Italy.
Phys Med Biol. 2005 Oct 21;50(20):4763-81. doi: 10.1088/0031-9155/50/20/002. Epub 2005 Sep 27.
Fetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available so far. In this paper, we describe an automatic method that restores the fetal cardiac trace from fMCG recordings by means of a weighted summation of fetal components separated with independent component analysis (ICA) and identified through dedicated algorithms that analyse the frequency content and temporal structure of each source signal. Multichannel fMCG datasets of 66 healthy and 4 arrhythmic fetuses were used to validate the automatic method with respect to a classical procedure requiring the manual classification of fetal components by an expert investigator. ICA was run with input clusters of different dimensions to simulate various MCG systems. Detection rates, true negative and false positive component categorization, QRS amplitude, standard deviation and signal-to-noise ratio of reconstructed fetal signals, and real and per cent QRS differences between paired fetal traces retrieved automatically and manually were calculated to quantify the performances of the automatic method. Its robustness and reliability, particularly evident with the use of large input clusters, might increase the diagnostic role of fMCG during the prenatal period.
胎儿心动图描记术（fMCG）可通过能够检索胎儿心脏信号的算法来监测胎儿心脏功能，但目前尚未有标准化的自动模型。在本文中，我们描述了一种自动方法，该方法通过对利用独立成分分析（ICA）分离出的胎儿成分进行加权求和，并通过分析每个源信号的频率成分和时间结构的专用算法来识别，从而从fMCG记录中恢复胎儿心脏轨迹。使用66例健康胎儿和4例心律失常胎儿的多通道fMCG数据集，相对于需要专家调查员手动分类胎儿成分的经典程序，对该自动方法进行验证。运行ICA时使用不同维度的输入聚类来模拟各种MCG系统。计算检测率、真阴性和假阳性成分分类、重建胎儿信号的QRS波幅、标准差和信噪比，以及自动和手动获取的配对胎儿轨迹之间的实际QRS波差异和百分比QRS波差异，以量化该自动方法的性能。其稳健性和可靠性，在使用大型输入聚类时尤为明显，这可能会增加fMCG在孕期的诊断作用。
