Jallouli Malika, Arfaoui Sabrine, Ben Mabrouk Anouar, Cattani Carlo
LATIS Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, Ecole Nationale d'Ingénieurs de Sousse, Sousse 4023, Tunisia.
Laboratory of Algebra, Number Theory and Nonlinear Analysis, Department of Mathematics, Faculty of Sciences, University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia.
Entropy (Basel). 2021 Jun 30;23(7):844. doi: 10.3390/e23070844.
Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. 2020, 62, 73-97, and Acta Appl. Math.. Additionally, classical Haar-Faber-Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se=100%, PPV=100% for FECG extraction and peak detection.
孕期胎儿心率分析对于监测胎儿的正常发育至关重要。当前的胎儿心率监测技术在胎儿心率监测和特征获取方面缺乏准确性，从而导致诊断方面的医学问题。挑战在于在孕期从母亲心电图中提取胎儿心电图。这种方法具有可靠且无创的优点。在本文中，提出了一种小波/多小波方法，以从母亲腹部心电图中完美提取胎儿心电图参数。第一步，由于进行了小波/多小波处理，应用了去噪程序来分离有噪声部分和去噪部分。去噪后的信号被认为是母亲心电图（MECG）和胎儿心电图（FECG）的混合。信息处理中一种广为人知的准确性度量是熵的概念。在本研究中，构建并应用了小波/多小波香农型熵来评估所提取的胎儿心电图信号的有序/无序程度。实验结果适用于在阿尔法维等人于2020年发表在《62卷，73 - 97页》以及《应用数学学报》上构建的一类最新的克利福德小波。此外，为了进行比较，还应用了经典的哈尔 - 法伯 - 肖德小波。使用了两个主要的知名数据库，即DAISY数据库和2013年CinC挑战赛数据库。在测试数据库上实现的准确性在胎儿心电图提取和峰值检测方面达到了灵敏度（Se）= 100%，阳性预测值（PPV）= 100%。
