Sharif Digital Repository

In this paper an efficient filtering procedure based on 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. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an... 

Background and objective: One of the important applications of non-invasive respiration monitoring using ECG signal is the detection of obstructive sleep apnea (OSA). ECG-derived respiratory (EDR) signals, contribute to useful information about apnea occurrence. In this paper, two EDR extraction methods are proposed, and their application in automatic OSA detection using single-lead ECG is investigated. Methods: EDR signals are extracted based on new respiration-related features in ECG beats morphology, such as ECG variance (EDRVar) and phase space reconstruction area (EDRPSR). After evaluating the EDRs by comparing them to a reference respiratory signal, they are used in an automatic OSA... 

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,... 

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... 

Electrocardiogram is one of the most prominent cardiac signals being capable to be utilized for medical uses such as arrhythmia detection. Over the years, the feasibility of using this signal for human identification issue has been investigated, and some methods have been proposed. In this Paper a novel approach is proposed for electrocardiogram (ECG) based human identification using Extended Kalman Filter (EKF). The innovation signal of EKF has been considered as feature which is used to classify different subjects. In this paper a general issue, human identification, is summarized to a classification problem in which the proposed features of each subject is calculated, and the... 

In this letter, we propose the application of the generalized eigenvalue decomposition for the decomposition of multichannel electrocardiogram (ECG) recordings. The proposed method uses a modified version of a previously presented measure of periodicity and a phase-wrapping of the RR-interval, for extracting the "most periodic" linear mixtures of a recorded dataset. It is shown that the method is an improved extension of conventional source separation techniques, specifically customized for ECG signals. The method is therefore of special interest for the decomposition and compression of multichannel ECG, and for the removal of maternal ECG artifacts from fetal ECG recordings. © 2006 IEEE  

An ECG signal is composed of many similar beats which makes it to behave semi-periodic. This paper deals with beat variation periods and exploits the correlation between cycles (inter-beat) and correlation within each cycle (intra-beat) for compression. For efficient compression a 2-dimensional array is constructed from the one dimensional ECG signal using decimation and interpolation in the discrete cosine transform (DCT) domain. Since reasonable results in image compression have been achieved by means of set partitioning in hierarchical trees (SPIHT) algorithm, we use SPIHT algorithm to code the 2-D wavelet transform of the ECG signals. Experimental results on selected records of ECG from... 

In order to access to an accurate detection of electrocardiogram signal in medical approaches especially mobile health and wearable medical devices, development of noise cancellation algorithms seems essential. In this study, the power line noise in ECG signal is filtered using several methods including DFT based, IIR, FIR, adaptive, Kalman, Wavelet and higher order statistics filters. Signal-to-noise ratio (SNR) improvements of the filters are then compared. It is found that FIR and IIR filtering show higher SNR improvement  

In this paper, we introduce a novel approach based on Tucker Decomposition and quasi-periodic nature of ECG signal for fetal ECG extraction from abdominal ECG mixture. We adapt variable periodicity constraint of the ECG components to main objective function of the Tucker Decomposition and shape it to matrix form in order to simply optimize the objective function. We form a 3rd order tensor by stacking the mixed multichannel ECG and reconstructed fetal and maternal subspaces using BSS methods in order to have the benefit of further artificial observations, and apply our proposed penalized decomposition on it. The proposed method is evaluated on synthetic and real datasets using the criteria... 

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called πCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of πCA (after maternal ECG cancellation), had a very good performance. Also,... 

False alarms ratios of up to 86% in Intensive Care Units (ICU) decrease quality of care, impacting both clinical staff and patients through slowing off response time and noise tribulation. We present a novel algorithm to predict heart arrhythmias in ICUs. We focus on five life-threatening arrhythmias: Asystole, Extreme Bradycardia, Extreme Tachycardia, Ventricular Tachycardia, and Ventricular Fibrillation. The algorithm is based on novel features using only 12 seconds of one ECG channel to predict the arrhythmias. Our new feature sets include different SQI and physiological features and the features used in obstructive sleep apnoea detection. We also proposed a new morphological... 

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 some recent works, model-based filtering approaches have been proved as effective methods for extracting ECG signals from single channel noisy recordings. The previously developed methods, use a highly realistic nonlinear ECG model for the construction of Bayesian filters. In this work, a multi-channel extension of the previous approach is developed, by using a three dimensional model of the cardiac dipole vector. The results have considerable improvement compared with the single channel approach. The method is hence believed to be applicable to low SNR multi-channel recordings  

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... 

Biomedical signals are acquired according to well-codified modalities. Space-time evolutions of their characteristics, correlated with clinical examination conditions, reveal the physiopathological state of the patient. These signals are non-stationary, and their non-stationary characteristics generally provide diagnostically useful information. Thus, the deployment of signal processing methodologies avoiding the assumption of stationarity is to be considered. During the last 20 years, time-frequency representations, particularly those belonging to Cohen's class, have been explored in various research fields within problems relevant to public health. Certain problems benefited from a new... 

Low and high valence were induced in 20 male volunteers using two groups of pictures stimuli. Heart response was compared between two groups from RR series extracted from recorded ECG measurements. Mean heart rate and heart rate variability measures including time, frequency and Poincare domain were extracted. The results revealed that HRV triangular index, SDNN and SD2 were the only statistically significant parameters between groups (p<0.05). Mean heart rate and power in LF and HF bands were also different between low and high valence groups however level of significance was not reached. © 2015 CCAL  

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... 

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