Taralunga Dragos Daniel, Ungureanu Mihaela, Hurezeanu Bogdan, Gussi Ilinca, Strungaru Rodica
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4134-7. doi: 10.1109/EMBC.2015.7319304.
The electrical activity of the uterus, i.e. the electrohysterogram (EHG), is one of the most prominent tool for preterm labour. There is no standard acquisition set up and often the EHG is corrupted with different types of noise: maternal and fetal electrocardiogram (mECG, fECG), electrical activity of the skeletal muscles, movement artifacts, power line interference (PLI) etc. Moreover, some of these noises overlap in frequency domain with the EHG. Thus, simple linear filtering approaches are not adequate. In this paper the empirical mode decomposition (EMD), a simple and data driven method, is proposed for EHG denoising. The method is evaluated on simulated data having different signal to noise ratios (SNRs) obtaining promising results.
子宫的电活动，即子宫电图（EHG），是早产最显著的工具之一。目前尚无标准的采集设置，并且EHG常常受到不同类型噪声的干扰：母体和胎儿心电图（mECG、fECG）、骨骼肌的电活动、运动伪影、电力线干扰（PLI）等。此外，其中一些噪声在频域上与EHG重叠。因此，简单的线性滤波方法并不适用。本文提出了一种简单的数据驱动方法——经验模态分解（EMD）用于EHG去噪。该方法在具有不同信噪比（SNR）的模拟数据上进行了评估，取得了有前景的结果。
