2,332 research outputs found
Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations
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.Web of Science71art. no. 20
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
A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator
PubMed ID: 26799770Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Web of Science37225623
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)
BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring
Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99%. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.Muhammad Asraful Hasan and Md Mamu
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
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