Real-Time Closed-Loop Suppression of Repolarization Alternans Reduces Arrhythmia Susceptibility In Vivo.

BACKGROUND: Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. METHODS: We have developed a real-time, closed-loop system to record and analyze RA from multiple intracardiac leads, and deliver dynamically R-wave triggered pacing stimuli during the absolute refractory period. We have evaluated the ability of this system to control RA and reduce arrhythmia susceptibility, in vivo. RESULTS: R-wave triggered pacing can induce RA, the magnitude of which can be modulated by varying the amplitude, pulse width, and size of the pacing vector. Using a swine model (n=9), we demonstrate that to induce a 1 µV change in the alternans voltage on the body surface, coronary sinus and left ventricle leads, requires a delivered charge of 0.04±0.02, 0.05±0.025, and 0.06±0.033 µC, respectively, while to induce a one unit change of the Kscore, requires a delivered charge of 0.93±0.73, 0.32±0.29, and 0.33±0.37 µC, respectively. For all body surface and intracardiac leads, both Δ(alternans voltage) and ΔKscore between baseline and R-wave triggered paced beats increases consistently with an increase in the pacing pulse amplitude, pulse width, and vector spacing. Additionally, we show that the proposed method can be used to suppress spontaneously occurring alternans (n=7), in the presence of myocardial ischemia. Suppression of RA by pacing during the absolute refractory period results in a significant reduction in arrhythmia susceptibility, evidenced by a lower Srank score during programmed ventricular stimulation compared with baseline before ischemia. CONCLUSIONS: We have developed and evaluated a novel closed-loop method to dynamically modulate RA in a swine model. Our data suggest that suppression of RA directly reduces arrhythmia susceptibility and reinforces the concept that RA plays a critical role in the pathophysiology of arrhythmogenesis.

A Novel Point-of-Care Smartphone Based System for Monitoring the Cardiac and Respiratory Systems.

Cardio-respiratory monitoring is one of the most demanding areas in the rapidly growing, mobile-device, based health care delivery. We developed a 12-lead smartphone-based electrocardiogram (ECG) acquisition and monitoring system (called "cvrPhone"), and an application to assess underlying ischemia, and estimate the respiration rate (RR) and tidal volume (TV) from analysis of electrocardiographic (ECG) signals only. During in-vivo swine studies (n = 6), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. Ischemic indices calculated from each lead showed statistically significant (p < 0.05) increase within 2 min of occlusion compared to baseline. Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 3) were preceded by significant (p < 0.05) increase of the ischemic index ~1-4 min prior to the onset of the tachy-arrhythmias. In order to assess the respiratory status during apnea, the mechanical ventilator was paused for up to 2 min during normal breathing. We observed that the RR and TV estimation algorithms detected apnea within 7.9 ± 1.1 sec and 5.5 ± 2.2 sec, respectively, while the estimated RR and TV values were 0 breaths/min and less than 100 ml, respectively. In conclusion, the cvrPhone can be used to detect myocardial ischemia and periods of respiratory apnea using a readily available mobile platform.

ECG segmentation and fiducial point extraction using multi hidden Markov model.

In this paper, we propose a novel method for extracting fiducial points (FPs) of electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model (MultiHMM) as opposed to the traditional use of Classic HMM. In the MultiHMM method, each segment of an ECG beat is represented by a separate ergodic continuous density HMM. Each HMM has different state number and is trained separately. In the test step, the log-likelihood of two consecutive HMMs is compared and a path is estimated, which shows the correspondence of each part of the ECG signal to the HMM with the maximum log-likelihood. Fiducial points are estimated from the obtained path. For performance evaluation, the Physionet QT database and a Swine ECG database are used and the proposed method is compared with the Classic HMM and a method based on partially collapsed Gibbs sampler (PCGS). In our evaluation using the QT database, we also compare the results with low-pass differentiation, hybrid feature extraction algorithm, a method based on the wavelet transform and three HMM-based approaches. For the Swine database, the root mean square error (RMSE) values, across all FPs for MultiHMM, Classic HMM and PCGS methods are 13, 21 and 40ms, respectively and the MultiHMM exhibits smaller error variability than other methods. For the QT database, RMSE values for MultiHMM, Classic HMM, Wavelet and PCGS methods are 10, 17, 26 and 38ms, respectively. Our results demonstrate that our proposed MultiHMM approach outperforms other benchmark methods that exist in the literature; therefore can be used in practical ECG fiducial point extraction.

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations.

In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 ms and 22 ms, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 ms and 29 ms, the proposed method achieves better accuracy and smaller variability with respect to other methods.

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.

A novel method to capture the onset of dynamic electrocardiographic ischemic changes and its implications to arrhythmia susceptibility.

BACKGROUND: This study investigates the hypothesis that morphologic analysis of intracardiac electrograms provides a sensitive approach to detect acute myocardial infarction or myocardial infarction-induced arrhythmia susceptibility. Large proportions of irreversible myocardial injury and fatal ventricular tachyarrhythmias occur in the first hour after coronary occlusion; therefore, early detection of acute myocardial infarction may improve clinical outcomes. METHODS AND RESULTS: We developed a method that uses the wavelet transform to delineate electrocardiographic signals, and we have devised an index to quantify the ischemia-induced changes in these signals. We recorded body-surface and intracardiac electrograms at baseline and following myocardial infarction in 24 swine. Statistically significant ischemia-induced changes after the initiation of occlusion compared with baseline were detectable within 30 seconds in intracardiac left ventricle (P<0.0016) and right ventricle-coronary sinus (P<0.0011) leads, 60 seconds in coronary sinus leads (P<0.0002), 90 seconds in right ventricle leads (P<0.0020), and 360 seconds in body-surface electrocardiographic signals (P<0.0022). Intracardiac leads exhibited a higher probability of detecting ischemia-induced changes than body-surface leads (P<0.0381), and the right ventricle-coronary sinus configuration provided the highest sensitivity (96%). The 24-hour ECG recordings showed that the ischemic index is statistically significantly increased compared with baseline in lead I, aVR, and all precordial leads (P<0.0388). Finally, we showed that the ischemic index in intracardiac electrograms is significantly increased preceding ventricular tachyarrhythmic events (P<0.0360). CONCLUSIONS: We present a novel method that is capable of detecting ischemia-induced changes in intracardiac electrograms as early as 30 seconds following myocardial infarction or as early as 12 minutes preceding tachyarrhythmic events.

An optimized method for estimating the tidal volume from intracardiac or body surface electrocardiographic signals: implications for estimating minute ventilation.

The ability to accurately monitor tidal volume (TV) from electrocardiographic (ECG) signals holds significant promise for improving diagnosis treatment across a variety of clinical settings. The objective of this study was to develop a novel method for estimating the TV from ECG signals. In 10 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the coronary sinus (CS), left ventricle (LV), and right ventricle (RV), as well as body surface electrograms, while TV was varied between 0 and 750 ml at respiratory rates of 7-14 breaths/min. We devised an algorithm to determine the optimized respirophasic modulation of the amplitude of the ECG-derived respiratory signal. Instantaneous measurement of respiratory modulation showed an absolute error of 72.55, 147.46, 85.68, 116.62, and 50.89 ml for body surface, CS, LV, RV, and RV-CS leads, respectively. Minute TV estimation demonstrated a more accurate estimation with an absolute error of 69.56, 153.39, 79.33, 122.16, and 48.41 ml for body surface, CS, LV, RV, and RV-CS leads, respectively. The RV-CS and body surface leads provided the most accurate estimations that were within 7 and 10% of the true TV, respectively. Finally, the absolute error of the bipolar RV-CS lead was significantly lower than any other lead configuration (P < 0.0001). In conclusion, we have demonstrated that ECG-derived respiratory modulation provides an accurate estimation of the TV using intracardiac or body surface signals, without the need for additional hardware.

An optimized method for the estimation of the respiratory rate from electrocardiographic signals: implications for estimating minute ventilation.

It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R(2) = 0.97), coronary sinus (R(2) = 0.96), left ventricular (R(2) = 0.96), and epicardial (R(2) = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R(2) = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R(2) = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware.

A translational approach to probe the proarrhythmic potential of cardiac alternans: a reversible overture to arrhythmogenesis?

Electrocardiographic alternans, a phenomenon of beat-to-beat alternation in cardiac electrical waveforms, has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death (SCD). In the clinical setting, a positive microvolt T-wave alternans test has been associated with a heightened risk of arrhythmic mortality and SCD during medium- and long-term follow-up. However, rather than merely being associated with an increased risk for SCD, several lines of preclinical and clinical evidence suggest that cardiac alternans may play a causative role in generating the acute electrophysiological substrate necessary for the onset of ventricular arrhythmias. Deficiencies in Ca(2+) transport processes have been implicated in the genesis of alternans at the subcellular and cellular level and are hypothesized to contribute to the conditions necessary for dispersion of refractoriness, wave break, reentry, and onset of arrhythmia. As such, detecting acute surges in alternans may provide a mechanism for predicting the impending onset of arrhythmia and opens the door to delivering upstream antiarrhythmic therapies. In this review, we discuss the preclinical and clinical evidence to support a causative association between alternans and acute arrhythmogenesis and outline the potential clinical implications of such an association.

T-wave alternans as an arrhythmic risk stratifier: state of the art.

Microvolt level T-wave alternans (MTWA), a phenomenon of beat-to-beat variability in the repolarization phase of the ventricles, has been closely associated with an increased risk of ventricular tachyarrhythmic events (VTE) and sudden cardiac death (SCD) during medium- and long-term follow-up. Recent observations also suggest that heightened MTWA magnitude may be closely associated with short-term risk of impending VTE. At the subcellular and cellular level, perturbations in calcium transport processes likely play a primary role in the genesis of alternans, which then secondarily lead to alternans of action potential morphology and duration (APD). As such, MTWA may play a role not only in risk stratification but also more fundamentally in the pathogenesis of VTE. In this paper, we outline recent advances in understanding the pathogenesis of MTWA and also the utility of T-wave alternans testing for clinical risk stratification. We also highlight emerging clinical applications for MTWA.

A novel method for determining the phase of T-wave alternans: diagnostic and therapeutic implications.

BACKGROUND: T-wave alternans (TWA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. However, to estimate and suppress TWA effectively, the phase of TWA must be accurately determined. METHODS AND RESULTS: We developed a method that computes the beat-by-beat integral of the T-wave morphology, over time points within the T-wave with positive alternans. Then, we estimated the signed derivative of the T-wave integral sequence, which allows the classification of each beat to a binary phase index. In animal studies, we found that this method was able to accurately identify the T-wave phase in artificially induced alternans (P<0.0001). The coherence of the phase increased consistently after acute ischemia induction in all body-surface and intracardiac leads (P<0.0001). Also, we developed a phase-resetting detection algorithm that enhances the diagnostic utility of TWA. We further established an algorithm that uses the phase of TWA to deliver appropriate polarity-pacing pulses (all interventions compared with baseline, P<0.0001 for alternans voltage; P<0.0001 for K(score)), to suppress TWA. Finally, we demonstrated that using the phase of TWA we can suppress spontaneous TWA during acute ischemia; 77.6% for alternans voltage (P<0.0001) and 92.5% for K(score) (P<0.0001). CONCLUSIONS: We developed a method to quantify the temporal variability of the TWA phase. This method is expected to enhance the utility of TWA in predicting ventricular arrhythmias and sudden cardiac death and raises the possibility of using upstream therapies to abort a ventricular tachyarrhythmia before its onset.

A novel pacing method to suppress repolarization alternans in vivo: implications for arrhythmia prevention.

BACKGROUND: Repolarization alternans (RA), a pattern of ventricular repolarization that repeats on an every other beat basis, has been closely linked with the substrate associated with ventricular tachycardia/ventricular fibrillation. OBJECTIVE: To evaluate a novel method to suppress RA. METHODS: We have developed a novel method to dynamically (on R-wave detection) trigger pacing pulses during the absolute refractory period. We have tested the ability of this method to control RA in a structurally normal swine heart in vivo. RESULTS: RA induced by triggered pacing can be measured from both intracardiac and body surface leads and the amplitude of R-wave triggered pacing-induced alternans can be locally modulated by varying the amplitude and width of the pacing pulse. We have estimated that to induce a 1 µV change in alternans voltage on the body surface, coronary sinus, and left ventricle leads, a triggered pacing pulse delivered in the right ventricle of 0.04±0.02, 0.05±0.025, and 0.06±0.033 µC, respectively, is required. Similarly, to induce a 1 unit change in Kscore (ratio of alternans peak to noise), a pacing stimulus of 0.93±0.73, 0.32±0.29, and 0.33±0.37 µC, respectively, is required. We have been able to demonstrate that RA can be suppressed by R-wave triggered pacing from a site that is within or across ventricles. Lastly, we have demonstrated that the proposed method can be used to suppress spontaneously occurring alternans in the diseased heart. CONCLUSION: We have developed a novel method to suppress RA in vivo.

Utility of a nonlinear joint dynamical framework to model a pair of coupled cardiovascular signals.

We have recently proposed a correlated model to provide a Gaussian mixture representation of the cardiovascular signals, with promising results in identifying rhythm disturbances. The approach provides a transformation of the data into a set of integrable Gaussians distributed over time. Looking into the model from a new joint modeling perspective, it is capable of assembling a filtered estimation, and can be used to derive temporal information of the waveforms. In this paper, we present a step-by-step derivation of the joint model putting correlation assumptions together to conclude a minimal joint description for a pair of ECG-ABP signals. We then probe novel applications of this model, including Kalman filter based denoising and fiducial point detection. In particular, we use the joint model for denoising and employ the denoised signals for pulse transit time (PTT) estimation. We analyzed more than 70 h of data from 76 patients from the MIMIC database to illustrate the accuracy of the algorithm. We have found that this method can be effectively used for robust joint ECG-ABP noise suppression, with mean signal-to-noise ratio (SNR) improvement up to 23.2 (12.0) dB and weighted diagnostic distortion measures as low as 2.1 (3.3)% for artificial (real) noises, respectively. In addition, we have estimated the error distributions for QT interval, systolic and diastolic blood pressure before and after filtering to demonstrate the maximal preservation of morphological features (ΔQT: mean ± std = 2.2 ± 6.1 ms; ΔSBP: mean ± std = 2.3 ± 1.9 mmHg; ΔDBP: mean ± std = 1.9 ± 1.4 mmHg). Finally, we have been able to present a systematic approach for robust PTT estimation (r = 0.98, p <; 0.001, mean ± std of error = -0.26 ± 2.93 ms). These findings may have important implications for reliable monitoring and estimation of clinically important features in clinical settings. In conclusion, the proposed framework opens the door to the possibility of deploying a hybrid system that integrates these algorithmic approaches for index estimation and filtering scenarios with high output SNRs and low distortion.

Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a Bayesian filter.

In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover, we propose a measure of signal fidelity by monitoring the covariance matrix of the innovation signals throughout the filtering procedure. Five categories of life-threatening arrhythmias were verified by simultaneously tracking the signal fidelity and the polar representation of the CV signal estimations. We analyzed data from Physiobank multiparameter databases (MIMIC I and II). Performance evaluation results demonstrated that the sensitivity of the detection ranges over 93.50% and 100.00%. In particular, the addition of more CV signals improved the positive predictivity of the proposed method to 99.27% for the total arrhythmic types. The method was also used for false arrhythmia suppression issued by ICU monitors, with an overall false suppression rate reduced from 42.3% to 9.9%. In addition, false critical ECG arrhythmia alarm rates were found to be, on average, 42.3%, with individual rates varying between 16.7% and 86.5%. The results illustrate that the method can contribute to, and enhance the performance of clinical life-threatening arrhythmia detection.

Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model.

In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of electrocardiogram (ECG) signals. It is shown that this model may be effectively used for generating synthetic ECGs as well as separate characteristic waves (CWs) such as the atrial and ventricular complexes. The model uses separate state variables for each CW, i.e. P, QRS and T, and hence is capable of generating individual synthetic CWs as well as realistic ECG signals. The model is therefore useful for generating arrhythmias. Simulations of sinus bradycardia, sinus tachycardia, ventricular flutter, atrial fibrillation and ventricular tachycardia are presented. In addition, discrete versions of the equations are presented for a model-based Bayesian framework for denoising. This framework, together with an extended Kalman filter and extended Kalman smoother, was used for denoising the ECG for both normal rhythms and arrhythmias. For evaluating the denoising performance, the signal-to-noise ratio (SNR) improvement of the filter outputs and clinical parameter stability were studied. The results demonstrate superiority over a wide range of input SNRs, achieving a maximum 12.7 dB improvement. Results indicate that preventing clinically relevant distortion of the ECG is sensitive to the number of model parameters. Models are presented which do not exhibit such distortions. The approach presented in this paper may therefore serve as an effective framework for synthetic ECG generation and model-based filtering of noisy ECG recordings.

Robust detection of premature ventricular contractions using a wave-based Bayesian framework.

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 allows the specification of a polar envelope for normal rhythms. Moreover, we propose a novel measure of signal fidelity by monitoring the covariance matrix of the innovation signals throughout the filtering procedure. PVCs are detected by simultaneous tracking the signal fidelity and the polar envelope. Five databases, including 40 records from MIT-BIH arrhythmia database, are used for differentiating normal, PVC, and other beats. Performance evaluation results show that the proposed method has an average detection accuracy of 99.10%, aggregate sensitivity of 98.77%, and aggregate positive predictivity of 97.47%. Furthermore, the method is capable of 100% accuracy for records that contain only PVCs and normal sinus beats. The results illustrate that the method can contribute to, and enhance the performance of clinical PVC detection.

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.

Model-based fiducial points extraction for baseline wandered electrocardiograms.

A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, 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, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining the precise position of the waveforms of an ECG signal with baseline wander is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and the unknown drift. A model-based approach handles these complications, therefore a method based on this concept has been developed and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model. Simulation results show that the proposed method has an average sensitivity of 99.58%, average detection accuracy of 99.64%, and specificity of 100%.

ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter.

In this paper an efficient filtering 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.