In this paper, we propose a technique of artificial intelligence called adaptive neuro fuzzy inference system (ANFIS) for canceling maternal electrocardiogram (MECG) in fetal electrocardiogram extraction (FECG).This technique is used to estimate the MECG present in the abdominal signal of a pregnant woman. The FECG is then extracted by subtracting the estimated MECG from the abdominal signal. P...

Background: Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant.Objective: Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a be...

This letter addresses the problem of fetal electrocardiogram (FECG) extraction. A class of source extraction algorithms is proposed, which uses a combination of two different approaches: Barros’s source extraction approach and Hyvärinen’s fixedpoint approach. The proposed algorithms can rapidly extract the desired FECG with little noise, and can work well in some bad situations. The validity an...

Fetal electrocardiogram (FECG) morphology plays an essential role in the early diagnosis of fetal health conditions. However, it is intractable to extract clean FECG signals, which are usually contaminated by maternal ECG (MECG) and various noises. To signals from non-invasive abdominal records, a high-performance high-efficient two-stage slow-fast long short-term memory (SFLSTM) based architec...

OBJECTIVES This paper describes a methodology for the monitoring of the fetal cardiac health status during pregnancy, through the effective and non-invasive monitoring of the abdominal ECG signals (abdECG) of the mother. METHODS For this purpose, a three-stage methodology has been developed. In the first stage, the fetal heart rate (fHR) is extracted from the abdECG signals, using nonlinear a...

Fetal monitoring during labour currently fails to accurately detect acidemia. We developed a method to assess the multidimensional properties of fetal heart rate variability (fHRV) from trans-abdominal fetal electrocardiogram (fECG) during labour. We aimed to assess this novel bioinformatics approach for correlation between fHRV and neonatal pH or base excess (BE) at birth.We enrolled a prospec...

in this paper, we propose a technique of artificial intelligence called adaptive neuro fuzzy inference system (anfis) for canceling maternal electrocardiogram (mecg) in fetal electrocardiogram extraction (fecg).this technique is used to estimate the mecg present in the abdominal signal of a pregnant woman. the fecg is then extracted by subtracting the estimated mecg from the abdominal signal. p...

In this paper we discussed the influence of preliminary processing of the ultrasound Doppler signal on accuracy of the fetal heart rate estimation as well as on reliability of the FHR instantaneous variability assessment. We attempted to develop an optimal processing channel of US Doppler signal in order to measure the periodicity of fetal heart activity with accuracy as close as possible to th...

OBJECTIVE To evaluate the feasibility of continuous telemetric trans-abdominal fetal electrocardiogram (a-fECG) in women undergoing labour induction at home. STUDY DESIGN Low risk women with singleton term pregnancy undergoing labour induction with retrievable, slow-release dinoprostone pessaries (n = 70) were allowed home for up to 24 hours, while a-fECG and uterine activity were monitored i...

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a low-power wireless body-area network for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing data with low power consumption. However, due to some specific characteristics of FECG recordings such as non-sparsity ...