Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

Modelling of the electrocardiographic signal during an angioplasty procedure in the right coronary artery

Dynamical models are useful tools to generate sets of varied morphological signals by synthesizing human electrocardiograms (ECGs). These signals are used for testing and improving algorithms of ECG delineation, patient monitoring and heart disease detection. This work presents a procedure based on the ECGSYN model to synthesize ECG morphological changes induced by a percutaneous transluminal coronary angioplasty (PTCA) procedure in the right coronary artery. We provide a set of parameters to be used in ECGSYN and generate heartbeats with altered ST-T complexes. These characteristic model parameters were obtained through a non-linear fitting algorithm applied to every available heartbeat. To extend these parameters, normal distributions were generated with their means and standard deviations obtained from the STAFF III database. Parameters were presented for P, QRS and T-waves at leads II, III and aVF. The synthesis procedure shows an average correlation and positive predictive value of 92.2% and 88.2%, respectively. In conclusion, we provide a technique capable of synthesizing electrocardiographic ischemic morphology with physiological plausibility. Then, the generation of data sets for algorithm testing can benefit from this system of ECG signal synthesis.

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements

OBJECTIVE

Compressive sensing (CS) has recently been applied as a low-complexity compression framework for long-term monitoring of electrocardiogram (ECG) signals using wireless body sensor networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat-to-beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in the case of compressed sensed data, signal reconstruction can be performed with relatively complex optimization algorithms, which may require significant energy consumption. This paper addresses the problem of heart rate estimation from CS ECG recordings, avoiding the reconstruction of the entire signal.

METHODS

We consider a framework, where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template.

RESULTS

Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals.

CONCLUSION

Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time low-power applications.

Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters

Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. F1 scores of 98.3% and 99.5% were found on each database, respectively, which are superior to other published algorithms' results reported on the same databases. Only 3% of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.

Comparison of three artificial models of the magnetohydrodynamic effect on the electrocardiogram

The electrocardiogram (ECG) is often acquired during magnetic resonance imaging (MRI), but its analysis is restricted by the presence of a strong artefact, called magnetohydrodynamic (MHD) effect. MHD effect is induced by the flow of electrically charged particles in the blood perpendicular to the static magnetic field, which creates a potential of the order of magnitude of the ECG and temporally coincident with the repolarisation period. In this study, a new MHD model is proposed by using MRI-based 4D blood flow measurements made across the aortic arch. The model is extended to several cardiac cycles to allow the simulation of a realistic ECG acquisition during MRI examination and the quality assessment of MHD suppression techniques. A comparison of two existing models, based, respectively, on an analytical solution and on a numerical method-based solution of the fluids dynamics problem, is made with the proposed model and with an estimate of the MHD voltage observed during a real MRI scan. Results indicate a moderate agreement between the proposed model and the estimated MHD model for most leads, with an average correlation factor of 0.47. However, the results demonstrate that the proposed model provides a closer approximation to the observed MHD effects and a better depiction of the complexity of the MHD effect compared with the previously published models, with an improved correlation (+5%), coefficient of determination (+22%) and fraction of energy (+1%) compared with the best previous model. The source code will be made freely available under an open source licence to facilitate collaboration and allow more rapid development of more accurate models of the MHD effect.

Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors

OBJECTIVE

We sought to evaluate the accuracy of a novel system for measuring fetal heart rate (FHR) and ST-segment changes using noninvasive electrodes on the maternal abdomen.

STUDY DESIGN

Fetal electrocardiograms were recorded using abdominal sensors from 32 term laboring women who had a fetal scalp electrode (FSE) placed for a clinical indication.

RESULTS

Good-quality data for FHR estimation were available in 91.2% of the FSE segments and 89.9% of the abdominal electrode segments. The root mean square error between the FHR data calculated by both methods over all processed segments was 0.36 beats per minute. ST deviation from the isoelectric point ranged from 0-14.2% of R-wave amplitude. The root mean square error between the ST change calculated by both methods averaged over all processed segments was 3.2%.

CONCLUSION

FHR and ST change acquired from the maternal abdomen is highly accurate and, on average, is clinically indistinguishable from FHR and ST change calculated using FSE data.

An artificial vector model for generating abnormal electrocardiographic rhythms

We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either as perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by introducing a rotation matrix couple to the respiratory frequency. We demonstrate an example of the use of this model by simulating HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods.

Robust parameter extraction for decision support using multimodal intensive care data

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.

ECG denoising and compression using a modified extended Kalman filter structure

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For denosing, the SNR improvement criterion is used, while for compression, we have considered the compression ratio (CR), the percentage area difference (PAD), and the weighted diagnostic distortion (WDD) measure. Several Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG databases are used for performance evaluation. Simulation results illustrate that both applications can contribute to and enhance the clinical ECG data denoising and compression performance. For denoising, an average SNR improvement of 10.16 dB was achieved, which is 1.8 dB more than the next benchmark methods such as MABWT or EKF2. For compression, the algorithm was extended to include more than five Gaussian kernels. Results show a typical average CR of 11.37:1 with WDD << 1.73%. Consequently, the proposed framework is suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions.

An Artificial Multi-Channel Model for Generating Abnormal Electrocardiographic Rhythms

We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat are incorporated as before from varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time-and frequency-domain heart rate (HR) and heart rate variability (HRV) characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by coupling the RR interval to the angular frequency of the dipole. We demonstrate an example of the use of this model by simulating T-Wave Alternans (TWA). The magnitude of the TWA effect is modeled as a disturbance on the T-loop of the dipole with a magnitude that differs in each of the three VCG planes. The effect is then turned on or off using a HMM. The values of the transition matrix are determined by the local heart rate, such that when the HR ramps up towards 100 BPM, the probability of observing a TWA effect rapidly but smoothly increases. In this way, no 'sudden' switching from non-TWA to TWA is observed, and the natural tendency for TWA to be associated with a critical HR-related activation level is simulated. Finally, to generate multi-lead signals, the VCG is mapped to any set of clinical leads using a Dower-like transform derived from a least-squares optimization between known VCGs and known lead morphologies. ECGs with calibrated amounts of TWA were generated by this model and included in the PhysioNet/CinC Challenge 2008 data set.

A mathematical algorithm for ECG signal denoising using window analysis

BACKGROUND

The presence of parasite interference signals could cause serious problems in the registration of ECG signals and many works have been done to suppress electromyogram (EMG) artifacts noises and disturbances from electrocardiogram (ECG). Recently, new developed techniques based on global and local transforms have become popular such as wavelet shrinkage approaches (1995) and time-frequency dependent threshold (1998). Moreover, other techniques such as artificial neural networks (2003), energy thresholding and Gaussian kernels (2006) are used to improve previous works. This review summarizes windowed techniques of the concerned issue.

METHODS AND RESULTS

We conducted a mathematical method based on two sets of information, which are dominant scale of QRS complexes and their domain. The task is proposed by using a varying-length window that is moving over the whole signals. Both the high frequency (noise) and low frequency (base-line wandering) removal tasks are evaluated for manually corrupted ECG signals and are validated for actual recorded ECG signals.

CONCLUSIONS

Although, the simplicity of the method, fast implementation, and preservation of characteristics of ECG waves represent it as a suitable algorithm, there may be some difficulties due to pre-stage detection of QRS complexes and specification of algorithm's parameters for varying morphology cases.

A nonlinear Bayesian filtering framework for ECG denoising

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.