De-noising of Fetal ECG for Fetal Heart Rate Calculation and Variability Analysis (original) (raw)
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An Algorithm for Fetal ECG Extraction from the Composite Abdominal Signal
2016
The electrical activity of the fetus heart is basically the fetal electrocardiogram.FECG is a weak signal which is measured indirectly by placing the electrode on the surface abdomen of the mother. The Fetal signals contains many other interfering signal. Extraction of the fetal ECG parameters from the abdominal signal has an important value in clinical application and also enables continuous monitoring of the fetus status by means of analyzing its cardiac activity. This paper proposes a non-invasive method for the FECG extraction by using a template for the cancellation of maternal QRS complex. The final results specify that the fetal R peaks can be easily detected under various circumstances without using the reference maternal thoracic signal. IndexTerms—AECG(AbdominalElectrocardiogram), FECG(Fetal Electrocardiogram), FHR(Fetal Heart Rate), MECG (Mother’s Electrocardiogram).
Proceedings
Here, we propose a signal processing based approach for the extraction of the fetal heart rate (FHR) from Maternal Abdominal ECG (MAECG) in a non-invasive way. Datasets from a Physionet database has been used in this study for evaluating the performance of the proposed model that performs three major tasks; preprocessing of the MAECG signal, separation of Fetal QRS complexes from that of maternal and estimation of Fetal R peak positions. The MAECG signal is first preprocessed with improved multistep filtering techniques to detect the Maternal QRS (MQRS) complexes, which are dominant in the MAECG. A reference template is then reconstructed based on MQRS locations and removed from the preprocessed signal resulting in the raw FECG. This extracted FECG is further corrected and enhanced before obtaining the Fetal R peaks. The detection of FQRS and calculation of FHR has been compared against the reference Fetal Scalp ECG. Results indicate that the approach achieved good accuracy.
Detection of Abnormalities in Fetal by non invasive Fetal Heart Rate Monitoring System
Fetal Heart rate is very important to obtain the information about the fetal condition during pregnancy. This is obtained by detecting the peaks of the FECG signal in R-R interval FECG is obtained here by extracting from the composite abdominal ECG. Extraction using advanced and powerful tools has been the ultimate interest in the biomedical field. Here the FECG is obtained from abdominal ECG by treating Maternal ECG as noise. Least Mean Square adaptive filter is used for this purpose. Noise like Power line interference and DC drift are removed by using notch filter and Chebyshev Filter respectively. Finally the peaks are counted for detecting abnormalities. Implementation is done using MATLAB GUI.
Methodological Survey on Fetal ECG Extraction
Abstract: Fetal Electrocardiogram (FECG) signal, non-invasively taken from the Abdominal Electrocardiogram (AECG) of a pregnant woman is a efficient diagnostic tool for evaluating the health status of fetus. Clinically significant information in the Fetal Electrocardiogram signal is often masked by Maternal Electrocardiogram (MECG) considered as the most predominant interference, power line interference, and maternal Electromyogram (EMG), baseline wander etc. Fetal Electrocardiogram signal features may not be readily comprehensible by the visual or auditory systems of a human. Therefore Fetal Electrocardiogram should be extracted from composite Abdominal Electrocardiogram for clinical diagnosis. There are many powerful and well advanced methods for this purpose. A methodological study has been carried out to show the effectiveness of various methods which helps in understanding of Fetal ECG signal and its analysis procedures by providing valuable information. Keywords: AbdominalECG, MaternalECG, ElectroMyoGram
An Automated Methodology for Fetal Heart Rate Extraction From the Abdominal Electrocardiogram
IEEE Transactions on Information Technology in Biomedicine, 2000
This paper introduces an automated methodology for the extraction of fetal heart rate from cutaneous potential abdominal ECG recordings. A three-stage methodology is proposed. Having the initial recording, which consists of a small number of abdominal ECG leads, in the first stage the maternal R-peaks and fiducial points (QRS onset and offset) are detected, using time-frequency analysis and medical knowledge. Then, the maternal QRS complexes are eliminated. In the second stage the positions of the candidate fetal R-peaks are located using complex wavelets and matching theory techniques. In the third stage, the fetal R-peaks which overlap with the maternal QRS complexes (eliminated in the first stage) are found using two approaches: a heuristic algorithm and a histogram-based technique. The fetal Rpeaks detected, are used to calculate the fetal heart rate. The methodology is validated using a dataset of 8 short and 10 long duration recordings, obtained between the 20th and the 41st week of gestation and the obtained accuracy is 97.47%. The proposed methodology is advantageous since it is based on the analysis of few abdominal leads, in contrast to other proposed methods which need a large number of leads.
A Novel Algorithm for the Arrhythmia Diagnosis in Fetal Monitoring System
2016
The objective of the work is to formulate and implement a new method for the extraction of Fetal ECG features from the abdominal ECG signal using Hybrid signal processing technique. Extraction of fetal ECG features and classification during first trimester of pregnancy will help the physician to know the well being of the fetal. Unwanted Noise signals significantly distort fetal ECG recordings, and consequently the presence of noises is troublesome in extracting the features in ECG signal. Hence, devising efficient methods for successful removal of noises and extraction of fetal ECG from ECG recordings have been still a major challenge. The performance of the algorithms employed previously for extraction of adult ECG signal and classification will not be efficient enough to do the same for the Fetal ECG. Therefore, an efficient algorithm to extract and classify fetal ECG is the objective of this work. In the method of recording, the fetal ECG signals have a very low power relative to that of the maternal ECG. In addition, there will be several sources of interference, which include intrinsic noise from a recorder, noise from electrode-skin contact, baseline drift (DC shift), 50/60 Hz noise etc.
Improved Method for Fetal Heart Rate Monitoring
2005
The fetal ECG can be detected in the recorded abdominal signals. A new procedure to compute the fetal heart rate (FHR) is proposed. The abdominal signal is first preprocessed in order to remove the baseline and the uterine contractions. Then the ECG of the mother (MECG) is removed using coherent averaging and optimizing the averaged MECG template. The channels containing the clearest fetal ECG signal (FECG) are identified by the autocorrelation function. The FECG is enhanced by the cross correlation between the two channels that show the strongest FECG. This enhancement is possible since the residual noise in the abdominal signal after removal of baseline, uterine contractions and maternal ECG is not correlated among the channels. The fetal R-Peaks are then detected and the FHR is computed. The obtained FHR is further corrected, using the information about the MECG and about the FECG.
A robust algorithm for fetal QRS detection using non-invasive maternal abdomen ECG
This paper presents an algorithm for automated fetal QRS (fQRS) detection. The algorithm was developed with the Fetal ECG (FECG) Challenge Database from PhysioNet. This database provides noninvasive ECG signals recorded from the mother's abdomen, and expert annotations for fQRS locations. Our algorithm consisted of four separate steps: 1. Maternal QRS complexes were detected using our QRS detector, featuring adaptive thresholds and automated, ECG-quality-based channel selection. 2. Maternal beat elimination by subtracting averaged maternal beats and by blanking maternal QRS complexes. 3. On the remaining signal QRS complex detection was applied with different parameter sets and detection quality was measured. 4. Finally, the parameter set leading to the highest fQRS detection quality was chosen and the detected fQRS sequences were optimized using statistical methods We achieved final scores of 82.413 for event 1 (MSE of fetal HR) and 7.354 for event 2 RMS of fetal RR) when participating in CinC Challenge 2013.
Problem statement: FECG (Fetal Electrocardiogram) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. Approach: Conventional techniques were often unable to achieve the extraction of FECG from the Abdominal ECG (AECG) in satisfactorily level. A new methodology by combining the Artificial Neural Network (ANN) and Correlation (ANNC) approach had been proposed in this study. Results: The accuracy of the proposed method for FECG extraction from the AECG signal was about 100% and the performance of the method for FHR extraction is 93.75%. Conclusions/Recommendations: The proposed approach involved the FECG extraction even though the MECG and FECG are overlapped in the AECG signal so that the physician and clinician can make the correct decision for the well-being of the fetus and mother during the pregnancy period.
Comparison of Cardiotocography and Fetal Heart Rate Estimators Based on Non-Invasive Fetal ECG
2019 Computing in Cardiology Conference (CinC)
Non-invasive fetal ECG (NI-FECG) extraction algorithms enable long-term continuous beat-to-beat monitoring of the fetal heart rate (FHR), as opposed to the gold standard in FHR monitoring, cardiotocography (CTG). We investigate how NI-FECG extraction algorithms selected from the CinC 2013 Challenge (CinC13) perform on data with low quality signals and how performance can be evaluated using CTG, when FQRS annotation is not possible. Four-channel NI-FECG was recorded simultaneously with a CTG trace on 22 pregnant women, gestational age 29-41 weeks. Seven algorithms were tested: The winning CinC13 entry from Varanini et al. and six algorithms from the unofficial top-scoring CinC13 entry by Behar et al. Two accuracy measures were used: 1) The RMSE between the FECG-based FHR and CTG traces; 2) The Pearson correlation coefficient r between the FECG-based FHR and CTG trace and its average over all recordings,r. The algorithms with the lowest RMSE's are Behar's FUSE-SMOOTH, a constant FHR, and Varanini, while the Varanini algorithm delivers the best correlation with the CTG trace (r = 0.73) with 41% of the recordings having r > 0.8, whereas the other algorithms haver ≤ 0.59 and ≤ 29% of the recordings with r > 0.8. FHR was estimated accurately in some recordings and poorly in others, believed to be due to large differences in signal quality.