Fetal ECG extraction from maternal abdominal ECG using neural network

FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG

Extracting Fetal Electrocardiogram from Being Pregnancy Based on Nonlinear Projection

Fetal heart rate extraction from the abdominal ECG is of great importance due to the information that carries in assessing appropriately the fetus well-being during pregnancy. In this paper, we describe a method to suppress the maternal signal and noise contamination to discover the fetal signal in a single-lead fetal ECG recordings. We use a locally linear phase space projection technique which has been used for noise reduction in deterministically chaotic signals. Henceforth, this method is capable of extracting fetal signal even when noise and fetal component are of comparable amplitude. The result is much better if the noise is much smaller (P wave and T wave can be discovered)

A clustering-based method for single-channel fetal heart rate monitoring

Non-invasive fetal electrocardiography (ECG) is based on the acquisition of signals from abdominal surface electrodes. The composite abdominal signal consists of the maternal electrocardiogram along with the fetal electrocardiogram and other electrical interferences. These recordings allow for the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper introduces a procedure for fetal heart rate extraction from a single-channel abdominal ECG signal. The procedure is composed of three main stages: a method based on wavelet for signal denoising, a new clustering-based methodology for detecting fetal QRS complexes, and a final stage to correct false positives and false negatives. The novelty of the procedure thus relies on using clustering techniques to classify singularities from the abdominal ECG into three types: maternal QRS complexes, fetal QRS complexes, and noise. The amplitude and time distance of all the local maxima followed by a local minimum were selected as features for the clustering classification. A wide set of real abdominal ECG recordings from two different databases, providing a large range of different characteristics, was used to illustrate the efficiency of the proposed method. The accuracy achieved shows that the proposed technique exhibits a competitve performance when compared to other recent works in the literature and a better performance over threshold-based techniques.This work has been partially funded by Banco Santander and Centro Mixto UGR-MADOC through project SIMMA (code 2/16). The contribution of Antonio García has been partially funded by Spain's Ministerio de Educación, Cultura y Deporte (Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, within Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016) under a "Salvador de Madariaga" grant (PRX17/00287)

Fetal ECG Extraction from Multichannel Abdominal ECG Recordings for Health Monitoring During Labor

AbstractExtracting clean fetal electrocardiogram (fECG) signals from non-invasive abdominal ECG recordings for monitoring the health of the fetus during pregnancy and labor remains a big challenge. The proposed system for facing extraction, processing and morphological feature estimation was implemented in LabVIEW 2013 with preinstalled Biosignal Filtering, Advanced Signal Processing and Digital Filter Design Toolboxes. The present approach is based on the using of FastICA algorithm for fECG extraction. In order to improve fECG extraction performance, it was applied here a combination of Undecimated Wavelet Transform (UWT) and Fast Fourier Transform (FFT) – Inverse Fast Fourier Transform (IFFT) algorithm as post-processing tool. Fetal ECG morphological indicators like heart rate, T/QRS ratio and QT interval could be estimated from fECG post-processed signals of two patients and some considerations regarding to the fetal stress during labor could be made in these two cases

Ekstraksi Fetal Electrocardiogram (FECG) Dari Abdominal ECG Menggunakan Metoda Wavelet

ABSTRAKSI: Akurasi deteksi sinyal jantung pada fetal (janin) selama kehamilan sangat potensial untuk menyediakan informasi kemungkinan adanya kelainan jantung pada janin. Dengan mengamati hasil standar electrocardiogram (ECG) yang diperoleh dengan cara merekam sinyal-sinyal elektrik pada sekitar perut sang ibu (abdomen), dapat diputuskan bahwa Fetal electrocardiogram memiliki daya yang relatif lebih lemah dari pada Maternal ECG, dan telah bercampur dengan beberapa penyebab interference (noise) sehingga akan mempersulit diagnosa [5].Suatu FECG terdiri dari komponen QRS complex, P wave dan T wave yang merupakan ciri-ciri dari gelombang sinyal tersebut dan mirip dengan ECG orang dewasa [7]. Untuk mendapatkan FECG murni diperlukan mekanisme menghilangkan noise-noise saat perekaman, di mana Maternal ECG dianggap salah satu noise dan paling berpengaruh Penulis akan mengekstrak FECG dari abdominal ECG dengan salah satu metoda yaitu Wavelet. Dimana Wavelet transform yang digunakan adalah DWT (Discrete Wavelet Transform).Dan pada tugas akhir ini, diinginkan gelombang sinyal fetal ECG yang dihasilkan memiliki bentuk yang bagus, dengan noise se-minimal mungkin. Metoda Wavelet menjadi alat untuk ekstraksi fetal ECG, sehingga bisa digunakan sebagai penyedia informasi untuk diagnosa kelainan pada janin. Kualitas sinyal hasil ekstraksi diukur dengan MSE di domain waktu dan frekuensi. Untuk percobaan dengan sinyal input abdominal 2, hasil ekstraksi terbaik adalah dengan wavelet coif1 level 4. Dan untuk percobaan dengan sinyal input abdominal 5, hasil ekstraksi terbaik adalah dengan wavelet db4 level 3.Kata Kunci : Fetal Electrocardiogram (FECG), Discrete Wavelet TransformABSTRACT: Accurate detection of fetal heart signals during pregnancy has the potential to provide information on possible fetal cardiac disease. By analyzing standard ECG recordings derived from leads placed on the abdominal, we determined that fetal signals have very low power relative to that of the maternal ECG, and they are mixed with several sources of interference (noise) so it can be obviously to diagnose [5]The FECG consist of QRS complex, P wave and T wave component that the characteristic of ECG waveform and morphologically similar to that of the adult ECG [7]. To gain pure FECG, some mechanisms to remove noises are needed during recordings, where maternal ECG is one of the noises and have the biggest contribution. We extract fetal ECG from the abdominal ECG with one of method, wavelet. Which Wavelet transform that used is DWT (Discrete Wavelet Transform).On this final assignment, we showed that the observed waveforms were not noise, but were actually the fetal ECG with good shape, with minimum noises. Wavelet method can be a powerful tool to extracts fetal ECG, so that used to provide information on diagnose cardiac disease. The quality of results extraction signal, are measured by MSE in time and frequency domain. For experiment with input signal abdominal 2, best result of extraction is with coif1 level 4. And for experiment with input signal abdominal 5, best result of extraction is with db4 level 3.Keyword: Fetal Electrocardiogram (FECG), Discrete Wavelet Transfor

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

Non-invasive Detection and Compression of Fetal Electrocardiogram

Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for single‐channel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), F‐score, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabah’s scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence

A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN

Monitoring the electrical pulse of fetal heart through a non-invasive fetal electrocardiogram (fECG) can easily detect abnormalities in the developing heart to significantly reduce the infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Our approach, which is based on 1D CycleGAN, can reconstruct the fECG signal from the mECG signal while maintaining the morphology due to extensive preprocessing and appropriate framework. The performance of our solution was evaluated by combining two available datasets from Physionet, "Abdominal and Direct Fetal ECG Database" and "Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations", where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus.Comment: 24 pages, 11 figure