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首页> 外文会议>IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference >Fetal Electrocardiogram Extraction Based on Fast ICA and Wavelet Denoising
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Fetal Electrocardiogram Extraction Based on Fast ICA and Wavelet Denoising

机译：基于快速ICA和小波去噪的胎儿心电图提取

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Fetal ECG can provide fetal health information to determine whether the fetal development is good. Before the birth of the fetus, indirect methods are usually used to obtain the abdomen electrocardiogram of pregnant women. However, due to the influence of maternal ECG and noise, blind source separation of fetal ECG is still challenging. The paper proposed a fetal electrocardiogram extraction method based on fast fixed-point algorithm. Here we are focusing on the independent component analysis based on negentropy. The ECG signals are picked up at the thoracic and abdominal region of the pregnant women. The paper analyses all abdominal ECG and one pectoral ECG to get fetal ECG. Then, the separated signal is reconstructed by principal component analysis. Finally, the reconstructed fetal ECG signal is further denoised by wavelet transform to improve the performance of the algorithm. The results show that the algorithm can effectively separate the fetal ECG from the ECG in pregnant women. The improved algorithm reduces the complexity of blind source separation, improves the robustness, and the signal separated is clear.
机译：胎儿心电图可以提供胎儿健康信息，以确定胎儿发育是否良好。在胎儿出生之前，通常使用间接方法来获取孕妇的腹部心电图。然而，由于孕妇心电图和噪声的影响，胎儿心电图的盲源分离仍然具有挑战性。提出了一种基于快速定点算法的胎儿心电图提取方法。在这里，我们专注于基于负熵的独立分量分析。心电图信号在孕妇的胸部和腹部区域采集。本文分析了所有腹部心电图和一个胸膜心电图，以获取胎儿心电图。然后，通过主成分分析来重构分离出的信号。最后，通过小波变换对重构的胎儿心电信号进一步去噪，以提高算法的性能。结果表明，该算法可以有效地将孕妇的胎儿心电图与心电图分离。改进后的算法降低了盲源分离的复杂度，提高了鲁棒性，分离出的信号清晰。

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