A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Kahánková, Radana; Martínek, Radek; Jaroš, René; Behbehani, Khosrow; Matonia, A.; Jeżewski, Michał; Behar, Joachim A.

doi:10.1109/rbme.2019.2938061

Cited by 84 publications

(108 citation statements)

References 160 publications

(220 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For several years now, there has been an increasing interest in monitoring bioelectric activity of the fetal heart 9,10 because an analysis of the fetal electrocardiogram (FECG) makes it possible to determine FHR with high efficiency and accuracy (Fig. 1).…”

Section: Background and Summarymentioning

confidence: 99%

“…This method is non-invasive and can be used both during pregnancy and labour. The method cannot be applied on a wider scale owing to the poor quality of the FECG signal recorded in the presence of numerous interferences 10,19 . Undoubtedly, the evaluation of its usefulness requires, above all, the effectiveness of the FQRS detection to be measured, which directly affects the continuity of FHR signal determination 18,26 .…”

Section: Technical Validationmentioning

confidence: 99%

“…The simulated data utilized the fecgsyn simulator 62 , which comprised seven varying physiological events, for ten different maternal and fetal heart dipole (2020) 7:200 | https://doi.org/10.1038/s41597-020-0538-z www.nature.com/scientificdata www.nature.com/scientificdata/ arrangements, at five noise levels. It should be noted that artificially modelled signals, even if the modelled parameters are changed in large ranges, will not fully reflect the nature of real abdominal signals, especially in the case of long-term pregnancy signals 10,14,18,42,43 .…”

Section: Technical Validationmentioning

confidence: 99%

See 2 more Smart Citations

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

et al. 2020

Self Cite

View full text Add to dashboard Cite

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. the resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Section: Technical Validationmentioning

confidence: 99%

Section: Technical Validationmentioning

confidence: 99%

See 1 more Smart Citation

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In NI-FECG, there has been technological challenges in robustly separating the fetal signal from the abdominal mixture because of the low signal to noise ratio of the fetal signal sensed on the abdomen and the presence of the isolating vernix caseosa layer during the third trimester of the pregnancy [14], [15]. However, recent advances in sensing technology and electrodes placement as well as advanced source separation algorithms have made this modality more and more attractive and many startups have obtained CE/FDA regulatory approval for their NI-FECG monitor [16].…”

Section: Alternative Monitoring Techniquesmentioning

confidence: 99%

Special Session on Computational Fetal Monitoring

Behar¹,

Weiner²,

Warrick³

2019

2019 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

Despite the improvements made in perinatal medicine during the 20th century, stillbirths still occur for 1 in 160 pregnancies in the US which represents a total of 26,000 fetal deaths each year. In addition, approximately 1 in 1000 fetuses experience oxygen deprivation during labor which is severe enough to cause brain injury. It is estimated that half of these cases of birth-related injury are preventable. Incorrect cardiotocography (CTG) interpretation is leading the list of causes. Intrapartum CTG is used routinely to measure maternal uterine pressure and fetal heart rate (FHR). Antepartum CTG monitoring is used to identify fetuses at risk of intrauterine hypoxia and acidaemia. As early as 28 weeks of gestation, analysis of the FHR trace is used as a nonstress test to assess the fetal well-being. In the perinatal period, timely, appropriate intervention can avoid fetal neurological damage or death. The CTG is visually assessed by a clinician or interpreted by computer analysis. In the context of labor monitoring, the CTG is used for continuous fetal monitoring. An abnormal heart rate will lead the clinician to perform a cesarean. With the recent advances in machine learning and statistical signal analysis new algorithms for assessing fetal antepartum or intrapartum health status are being elaborated. These algorithms process signals recorded by CTG monitors or alternative monitoring techniques such as scalp electrocardiography or non-invasive fetal electrocardiography. This session discusses the history of fetal monitoring, its current challenges and the prospects opened by recent algorithmic development.

show abstract

“…Blind source separation (BSS) methods, template subtraction (TS), and filtering techniques are popular approaches used to extract fECG [6]. The BSS methods assume that the abdominal signal is a combination of independent sources, including fECG, mECG, and noises [7].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram

Rahmani

et al. 2020

Sensors

View full text Add to dashboard Cite

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

show abstract

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Cited by 84 publications

References 160 publications

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Special Session on Computational Fetal Monitoring

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram

Contact Info

Product

Resources

About