Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation
Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation
复制标题
使用信号到信号转换消除腹部心电图记录中的胎动
DOI：
10.1109/embc48229.2022.9871826
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Tavassolian, Negar
中科院分区：
文献类型：
--
作者：
Shokouhmand, Arash;Tavassolian, Negar
本研究旨在利用深度神经网络来消除腹部心电记录中的胎动。为此，一个由两个耦合发生器组成的产生式信号到信号转换模型被用来发现胎儿运动污染的心电记录和干净的心电记录之间的关系。该模型是在胎儿心电合成数据库(FECGSYNDB)上训练的，该数据库提供10个孕妇的ACG记录及其基本真实的孕妇和胎儿心电信号。信号最初被分割成4秒的片段，然后馈入网络进行去噪。实验结果表明，这种信号-信号转换方法可以重建出干净的心电信号，其平均绝对误差、均方根偏差和皮尔逊相关系数分别为0.099、0.124和99.12%。此外，对于(-3，3)d B范围内的信噪比，该方法对低信噪比输入值的均方根标准偏差范围为(0.047，0.352)，表明了该方法的鲁棒性。临床相关性-建议的框架允许对腹部心电信号进行去噪，以进行非侵入性胎儿心率监测。由于使用了先进的神经网络技术，该方法是准确的
This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals. The signals are initially segmented into 4-second segments and then fed into the network for denoising. It is demonstrated that the signal-to-signal translation method can reconstruct clean AECG signals with average mean-absolute-error (MAE), root-mean-square deviation (RMSD), and Pearson correlation coefficient (PCC) of 0.099, 0.124, and 99.12% respectively, between clean and denoised AECG signals. Furthermore, the robustness of the method to low signal-to-noise ratio (SNR) input values is shown by an RMSD range of (0.047, 0.352) for SNR values within the range of (-3, 3) dB. Clinical Relevance- The proposed framework allows for the denoising of abdominal ECG signals for non-invasive fetal heart rate monitoring. The approach is accurate due to the use of advanced neural network techniques
可穿戴腹部惯性传感器提取胎心率的初步研究
影响因子：
4.3
作者：
Yang, Chenxi;Antoine, Clarel;Young, Bruce K.;Tavassolian, Negar
通讯作者：
Tavassolian, Negar
胎心率估计的多模态框架：低信噪比心电图和惯性传感器的融合
DOI：
10.1109/embc46164.2021.9629975
发表时间：
2021
期刊：
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
影响因子：
--
作者：
Shokouhmand, Arash;Antoine, Clarel;Young, Bruce K.;Tavassolian, Negar
通讯作者：
Tavassolian, Negar
关于死产。
DOI：
--
发表时间：
1982
期刊：
Journal of Pastoral Care
影响因子：
--
作者：
E. Kirkley;K. R. Kellener;S. Gould;W. Donnelly
通讯作者：
W. Donnelly