A signal analysis procedure is described for obtaining time intervals parameters of the fetal electrocardiogram as recorded from the maternal abdomen. Applying averaging to the fetal electrocardiogram quantification of the PR interval, QRS duration and QT interval were measured. This technique which includes the subtraction of an averaged maternal ECG waveform using cross-correlation function and fast Fourier transform algorithm, enables the detection of all the fetal QRS complexes in spite of their coincidence with the maternal ECGs. Results that were obtained from 21 pregnant women at the gestational age of 32-41 weeks and an example of a recording with fetal premature ventricular contractions are presented. This method shows an important improvement with respect to detection of fetal heart rate and detection of arrhythmia disturbances in the fetal ECG. The averaging procedure can be used to evaluate long-lived alterations in the fetal ECG. 相似文献
A real-time fetal ECG monitoring system using abdominal recording is presented. The system is based on an IBM AT compatible personal computer. The computer lacks the performance required for real-time analysis. Therefore, a new design of a fast hardware correlator board was developed to enhance the computer throughput. The technique is based on a cross-correlation procedure. An averaged maternal ECG waveform is derived using the cross-correlation function for the waveform's alignment. With this procedure a template signal corresponding to one complete maternal ECG is obtained. The averaged maternal ECG is then subtracted from the abdominal signals. Thus, it is possible to detect all the fetal ORS complexes in spite of their coincidence with the maternal ECG. An average fetal ECG is then extracted to improve the signal-to-noise ratio, making it possible to recognize fetal P and T waves. 相似文献
The spectral curves of the averaged fetal and maternal electrocardiograms as recorded from the abdomen were studied. The power spectrums were obtained using a technique which includes the subtraction of an averaged maternal ECG waveform using cross-correlation function and fast Fourier transform algorithm. The spectral curves of the averaged maternal and fetal ECG waveforms obtained from 21 pregnant women who had gestation periods of 32–41 weeks were studied. It was found that the poor signal to noise ratio, the high rate of coincidence between maternal and fetal ECGs and the similar frequency spectra of the signal and the noise components make an analysis of the abdominal ECG using conventional filtering technique rarely possible and an alternative method should be used. 相似文献
Bioelectrical fetal heart activity being recorded from maternal abdominal surface contains more information than mechanical heart activity measurement based on the Doppler ultrasound signals. However, it requires extraction of fetal electrocardiogram from abdominal signals where the maternal electrocardiogram is dominant. The simplest technique for maternal component suppression is a blanking procedure, which relies upon the replacement of maternal QRS complexes by isoline values. Although, in case of coincidence of fetal and maternal QRS complexes, it causes a loss of information on fetal heart activity. Its influence on determination of fetal heart rate and the variability analysis depends on the sensitivity of the heart-beat detector used. The sensitivity is defined as an ability to detect the incomplete fetal QRS complex. The aim of this work was to evaluate the influence of the maternal electrocardiogram suppression method used on the reliability of FHR signal being calculated. 相似文献
Extraction of a clean fetal electrocardiogram (ECG) from non-invasive abdominal recordings is one of the biggest challenges in fetal monitoring. An ECG allows for the interpretation of the electrical heart activity beyond the heart rate and heart rate variability. However, the low signal quality of the fetal ECG hinders the morphological analysis of its waveform in clinical practice. The time-sequenced adaptive filter has been proposed for performing optimal time-varying filtering of non-stationary signals having a recurring statistical character. In our study, the time-sequenced adaptive filter is applied to enhance the quality of multichannel fetal ECG after the maternal ECG is removed. To improve the performance of the filter in cases of low signal-to-noise ratio (SNR), we enhance the ECG reference signals by averaging consecutive ECG complexes. The performance of the proposed augmented time-sequenced adaptive filter is evaluated in both synthetic and real data from PhysioNet. This evaluation shows that the suggested algorithm clearly outperforms other ECG enhancement methods, in terms of uncovering the ECG waveform, even in cases with very low SNR. With the presented method, quality of the fetal ECG morphology can be enhanced to the extent that the ECG might be fit for use in clinical diagnostics.
Graphical abstract The extracted fetal ECG signals from non-invasive abdominal recordings still contain a substantial amount of noise. The time-sequenced adaptive filter provides a relatively accurate estimate of the underlying fetal ECG signal when the quality of the reference channels is enhanced prior to filtering.
This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier. 相似文献
This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier. 相似文献
Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals. 相似文献
This paper describes a method for detecting low-level fetal ECG signals in maternal abdominal ECG recordings. Detection is based on a systematic application of the principle that the fetal ECG contains proportionately greater high-frequency components than does the maternal ECG. Adaptive subtraction of the maternal high-frequency components is used to detect the fetal R-waves. The method is found to detect the fetal ECG even in many cases where the maternal and fetal R-waves coincide or occur in close proximity to each other. Recursive time-coherent averaging is then used to significantly improve the signal-to-noise ratio of the fetal ECG to the point where the fetal P and T waves may be observed. 相似文献
The present work describes fast computation methods for real-time digital filtration and QRS detection, both applicable in
autonomous personal ECG systems for long-term monitoring. Since such devices work under considerable artifacts of intensive
body and electrode movements, the input filtering should provide high-quality ECG signals supporting the accurate ECG interpretation.
In this respect, we propose a combined high-pass and power-line interference rejection filter, introducing the simple principle
of averaging of samples with a predefined distance between them. In our implementation (sampling frequency of 250 Hz), we
applied averaging over 17 samples distanced by 10 samples (Filter10x17), thus realizing a comb filter with a zero at 50 Hz and high-pass cut-off at 1.1 Hz. Filter10x17 affords very fast filtering procedure at the price of minimal computing resources. Another benefit concerns the small ECG
distortions introduced by the filter, providing its powerful application in the preprocessing module of diagnostic systems
analyzing the ECG morphology. Filter10x17 does not attenuate the QRS amplitude, or introduce significant ST-segment elevation/depression. The filter output produces
a constant error, leading to uniform shifting of the entire P-QRS-T segment toward about 5% of the R-peak amplitude. Tests
with standardized ECG signals proved that Filter10x17 is capable to remove very strong baseline wanderings, and to fully suppress 50 Hz interferences. By changing the number of
the averaged samples and the distance between them, a filter design with different cut-off and zero frequency could be easily
achieved. The real-time QRS detector is designed with simplified computations over single channel, low-resolution ECGs. It
relies on simple evaluations of amplitudes and slopes, including history of their mean values estimated over the preceding
beats, smart adjustable thresholds, as well as linear logical rules for identification of the R-peaks in real-time. The performance
of the QRS detector was tested with internationally recognized ECG databases (AHA, MIT-BIH, European ST-T database), showing
mean sensitivity of 99.65% and positive predictive value of 99.57%. The performance of the presented QRS detector can be highly
rated, comparable and even better than other published real-time QRS detectors. Examples representing some typical unfavorable
conditions in real ECGs, illustrate the common operation of Filter10x17 and the QRS detector. 相似文献
A method based on signal entropy is proposed for the detection of QRS complexes in the 12-lead electrocardiogram (ECG) using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander in the ECG signal. Combined Entropy criterion was used to enhance the QRS complexes. SVM is used as a classifier to delineate QRS and non-QRS regions. The performance of the proposed algorithm was tested using 12-lead real ECG recordings from the standard CSE ECG database. The numerical results indicated that the algorithm achieved 99.93% of detection rate. The percentage of false positive and false negative is 0.54% and 0.06%, respectively. The proposed algorithm performs better as compared with published results of other QRS detectors tested on the same database. 相似文献
In this paper, an algorithm based on independent component analysis (ICA) for extracting the fetal heart rate (FHR) from maternal abdominal electrodes is presented. Three abdominal ECG channels are used to extract the FHR in three steps: first preprocessing procedures such as DC cancellation and low-pass filtering are applied to remove noise. Then the algorithm for multiple unknown source extraction (AMUSE) algorithm is fed to extract the sources from the observation signals include fetal ECG (FECG). Finally, FHR is extracted from FECG. The method is shown to be capable of completely revealing FECG R-peaks from observation leads even with a SNR=-200dB using semi-synthetic data. 相似文献
How to extract information intensively from ECGs for the diagnosis of cardiovascular diseases and assessment of heart function
is a topical subject. Using a method based on the wavelet transform to calculate the irregularity of the QRS complex, which
may relate to inotropy, the QRS complex irregularity time series is successfully extracted from original ECG signals. This
provides a new approach to studies of ECG dynamics. With the help of non-linear dynamics theory, the QRS complex irregularity
time series of eight subjects, from the MIT/BIH arrhythmia database are studied qualitatively and quantitatively, and the
characteristics of ECG dynamics are analysed extensively. The power spectrum, phase portrait, correlation dimension, largest
Lyapunov exponent, time-dependent divergence exponent and complexity measure all verify the fact that ECG dynamics are dominated
by an underiying 5–6-dimensional non-linear chaotic system, whose complexity measure is about 0.7. The QRS complex irregularity
time series contains abundant information about all parts of the heart and the regulation of the autonomic nervous system,
and so further analyses are of great potential theoretical and clinical significance to patho-physiology studies and ambulatory
monitoring. 相似文献
In this paper, a fractional digital differentiation-based algorithm for detecting R wave in QRS complex of electrocardiogram (ECG) is developed. A FIR bandpass filter, whose coefficients only depend on fractional orders, reduces various noises present in ECG signals and generates peaks corresponding to the ECG parts with high slopes. This filter is followed by nonlinear transforms and smoothing to enhance peaks corresponding to R waves. Algorithm tests on the Massachusetts Institute of Technology/Beth Israel Hospital (MIT/BIH) ECG database illustrate the capability of this novel approach to recognizing QRS complexes in very noisy ECG signals. The algorithm’s performances are comparable to those of the most efficient QRS detectors tested on this database. 相似文献