Sharif Digital Repository

In this paper, we propose an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. The proposed method is performed in two steps: first, we employ a robust tensor decomposition-based method for fetal ECG extraction, assuming different heart rates for mother and fetal ECG; then a method based on extended Kalman filter (EKF) in which the ECG beat is modeled by 3 state equations (P, QRS and T), is used for fetal R-peak detection. The results show that the proposed method is efficiently able to estimate the location of R-peaks of fetal ECG signals. The obtained average scores of event 4 and 5 on... 

Fetal electrocardiogram (fECG) monitoring is a beneficial method for assessing fetal health and diagnosing the fetal cardiac condition during pregnancy. In this study, an algorithm is proposed to extract fECG from maternal abdominal signals based on doubly constrained block-term (DoCoBT) tensor decomposition. This tensor decomposition method is constrained by quasi-periodicity constraints of fetal and maternal ECG signals. Tensor decompositions are more powerful tools than matrix decomposition, due to employing more information for source separation. Tensorizing abdominal signals and using periodicity constraints of fetal and maternal ECG, appropriately separates subspaces of the mother, the... 

In this paper an efficient Altering procedure based on the Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics. © 2007 IEEE  

In this paper, we proposed a method based on Kalman Filter for predicting the onset of paroxysmal atrial fibrillation (PAF) from the electrocardiogram (ECG) using clinical data available from the Computers in Cardiology (CinC) Challenge 2001. To predict PAF, we developed an algorithm based upon the number of atrial premature complexes (APCs) in the ECG. The algorithm detects classical isolated APCs by monitoring fidelity signals, which is defined here as a function of the innovation signal of Kalman filter, in vicinity of premature heartbeats and decides whether one beat is APC or not then predicts PAF, based on the number of APC. The challenge database consists of 56 pairs of 30-minute ECG... 

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are... 

This paper studies the subject of adaptive noise cancelation using the Kalman filtering technique to achieve high precision and fast convergence. It is shown that the Kalman filter can successfully be designed to detect and extract periodic noises which may be constituted of different sinusoidal components with possibly unknown and/or time-varying frequencies. This highlights the feature of Kalman filter in synthesizing periodic noises in the time-domain which is not possible using Fourier-based methods such as DFT. Usefulness of the method is discussed in the context of two examples: active cancelation of periodic noises from audio waveforms and filtering of electrocardiogram measurements.... 

In this study, surface ECG recordings have been used to accomplish a non-invasive method which can predict spontaneous termination of Atrial Fibrillation (AF) and discriminate terminating (T) and non-terminating (N) AF episodes. The data set was provided by Physionet including holter recordings of 50 patients (20 training and 30 test sets). Concerning that most relevant information about the AF exists in the atrial fibrillatory wave, Several spectral and time-frequency parameters were extracted from the ECG signal after canceling the QRST complex. Also a temporal feature, RR interval variation, representing the ventricular activity was calculated. These parameters were evaluated using a... 

We have presented a new method for ECG baseline correction using the adaptive bionic wavelet transform (BWT). In fact by the means of BWT, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying our previous thresholding rule, one can handle ECG baseline correction. First an estimation of the baseline wandering frequency is obtained and then the adaptation can be used only in three successive scales in which the mid-scale has the closest center frequency to the estimated frequency. Thus the... 

This paper presents a multi-stage algorithm for QRS complex classification into normal and abnormal categories using an unsupervised sequential beat clustering and a cross-distance analysis algorithm. After the sequential beat clustering, a search algorithm based on relative similarity of created classes is used to detect the main normal class. Then other classes are labeled based on a distance measurement from the main normal class. Evaluated results on the MIT-BIH ECG database exhibits an error rate less than 1% for normal and abnormal discrimination and 0.2% for clustering of 15 types of arrhythmia existing on the MIT-BIH database. © 2001 IEEE  

An improved compression algorithm for ECG signals is presented using temporal alignment of beats and 2-D wavelet packet transform (WPT). This 2-D transform based approach utilizes the fact that the electrocardiogram (ECG) signals generally show two types of correlation, namely correlation between subsequent samples within each ECG cycle (intrabeat) and correlation between subsequent cycles (interbeat). One simple compression algorithm in the 2-D WPT domain, which is applied to some records in the MIT-BIH arrhythmia database shows lower percent root mean square difference (PRD) than 1-D wavelet based compression methods for the same compression ratio (CR). © 2001 IEEE  

In this Paper a new approach is proposed for electrocardiogram (ECG) based human identification using wavelet distance measurement. The main advantage of this method is that it guarantees high accuracy even in abnormal cases. Furthermore, it possesses low sensitivity to noise. The algorithm was applied on 11 normal subjects and 10 abnormal subjects of MIT-BIH Database using single lead data, and a 100% human identification rate was on both normal and abnormal subjects. Adding artificial white noise to signals shows that the method is nearly accurate in SNR level above 5dB in normal subjects and 20dB in abnormal subjects  

The noninvasive fetal electrocardiogram (ECG) data available from Physionet data bank are suitable for developing fetal heart rate (FHR) detection algorithms. The data have been collected from single subject with a broad range of gestation weeks, and have a total data length of more than 9 hours arranged in 55 data sets. However, there are three additional data features which are currently not directly available from Physionet to facilitate the easy usage of these data: (1) the fetal peak visibility evaluation, (2) the gestation week, and (3) the data length. This article presents an improvement to the data bank by providing the additional features. The required pre-processing of the data is... 

Detection and classification of ventricular complexes from the ECG is of considerable importance in Holter and critical care patient monitoring, being essential for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in relation to life-threatening arrhythmias. In this paper, we introduce a model-based dynamic algorithm for tracking the ECG characteristic waveforms using an extended Kalman filter. The algorithm can work on single or multiple leads. A "polargram"--a polar representation of the signal--is introduced, which is constructed using the Bayesian estimations of the state variables. The polargram... 

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal  

In this paper, an algorithm for ECG denoising and compression based on a sparse separable 2-dimensional transform for both complete and overcomplete dictionaries is studied. For overcomplete dictionary we have used the combination of two complete dictionaries. The experimental results obtained by the algorithm for both complete and overcomplete transforms are compared to soft thresholding (for denoising) and wavelet db9/7 (for compression). It is experimentally shown that the algorithm outperforms soft thresholding for about 4dB or more and also outperforms Extended Kalman Smoother filtering for about 2dB in higher input SNRs. The idea of the algorithm is also studied for ECG compression,... 

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals. © 2008 Institute of Physics and Engineering in Medicine  

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the T-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real... 

A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term... 

In recent studies, Independent Component Analysis (ICA) has been used for the analysis of multi-channel ECG recordings. However most of these works have been carried out from the signal processing perspective. In this work, the single dipole vector theory of the heart and the ECG dimensionality are studied from the source separation viewpoint. Based on this study, the interpretation of the components extracted from multi-channel ECG and maternal abdominal recordings, and their relationship with the vectorcardiogram representation of the cardiac dipole are presented. The results of this study can be used for the extraction of meaningful clinical indexes, based on ICA techniques. © 2006 IEEE