On the efficiency and accuracy of the single equivalent moving dipole method to identify sites of cardiac electrical activation

We have proposed an algorithm to guide radiofrequency catheter ablation procedures. This algorithm employs the single equivalent moving dipole (SEMD) to model cardiac electrical activity. The aim of this study is to investigate the optimal time instant during the cardiac cycle as well as the number of beats needed to accurately estimate the location of a pacing site. We have evaluated this algorithm by pacing the ventricular epicardial surface and inversely estimating the locations of pacing electrodes from the recorded body surface potentials. Two pacing electrode arrays were sutured on the right and left ventricular epicardial surfaces in swine. The hearts were paced by the electrodes sequentially at multiple rates (120–220 bpm), and body surface ECG signals from 64 leads were recorded for the SEMD estimation. We evaluated the combined error of the estimated interelectrode distance and SEMD direction at each time instant during the cardiac cycle, and found the error was minimum when the normalized root mean square (RMS[subscript n]) value of body surface ECG signals reached 15 % of its maximum value. The beat-to-beat variation of the SEMD locations was significantly reduced (p < 0.001) when estimated at 15 % RMS[subscript n] compared to the earliest activation time (EAT). In addition, the 5–95 % interval of the estimated interelectrode distance error decreased exponentially as the number of beats used to estimate a median beat increased. When the number of beats was 4 or larger, the 5–95 % interval was smaller than 3.5 mm (the diameter of a commonly used catheter). In conclusion, the optimal time for the SEMD estimation is at 15 % of RMS[subscript n], and at that time instant a median beat estimated from 4 beats is associated with a beat-to-beat variability of the SEMD location that is appropriate for catheter ablation procedures.National Institutes of Health (U.S.) (grant 1RO1HL103961

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

International audienceIn 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 msec and 22 msec, 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 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods. Keywords: Electrocardiogram (ECG), Extended Kalman Filter (EKF), Dynamic Time Warping (DTW), Fiducial Point Extraction, Denoising

Cardiac mitochondria exhibit dynamic functional clustering

Multi-oscillatory behavior of mitochondrial inner membrane potential ΔΨm in self-organized cardiac mitochondrial networks can be triggered by metabolic or oxidative stress. Spatio-temporal analyses of cardiac mitochondrial networks have shown that mitochondria are heterogeneously organized in synchronously oscillating clusters in which the mean cluster frequency and size are inversely correlated, thus suggesting a modulation of cluster frequency through local inter-mitochondrial coupling. In this study, we propose a method to examine the mitochondrial network's topology through quantification of its dynamic local clustering coefficients. Individual mitochondrial ΔΨm oscillation signals were identified for each cardiac myocyte and cross-correlated with all network mitochondria using previously described methods (Kurz et al., 2010a). Time-varying inter-mitochondrial connectivity, defined for mitochondria in the whole network whose signals are at least 90% correlated at any given time point, allowed considering functional local clustering coefficients. It is shown that mitochondrial clustering in isolated cardiac myocytes changes dynamically and is significantly higher than for random mitochondrial networks that are constructed using the Erdös–Rényi model based on the same sets of vertices. The network's time-averaged clustering coefficient for cardiac myocytes was found to be 0.500 ± 0.051 (N = 9) vs. 0.061 ± 0.020 for random networks, respectively. Our results demonstrate that cardiac mitochondria constitute a network with dynamically connected constituents whose topological organization is prone to clustering. Cluster partitioning in networks of coupled oscillators has been observed in scale-free and chaotic systems and is therefore in good agreement with previous models of cardiac mitochondrial networks

A novel technique for guiding ablative therapy of cardiac arrhythmias

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1999.Includes bibliographical references (leaves 173-180).by Antonis A. Armoundas.Ph.D

Method and apparatus for guiding ablative therapy of abnormal biological electrical excitation

This invention involves method and apparatus for guiding ablative therapy of abnormal biological electrical excitation. In particular, it is designed for treatment of cardiac arrhythmias. In the method of this invention electrical signals are acquired from passive electrodes, and an inverse dipole method is used to identify the site of origin of an arrhytmia. The location of the tip of the ablation catheter is similarly localized from signals acquired from the passive electrodes while electrical energy is delivered to the tip of the catheter. The catheter tip is then guided to the site of origin of the arrhythmia, and ablative radio frequency energy is delivered to its tip to ablate the site

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Microvolt T-wave alternans as a predictor of mortality and severe arrhythmias in patients with left-ventricular dysfunction: a systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Studies have demonstrated that the use of implantable cardioverter defibrillators (ICDs) is effective for the primary prevention of arrhythmic events but due to imposing costs, there remains a need to identify which patients will derive the greatest benefit. Microvolt T-wave alternans (MTWA) has been proposed to assist in this stratification.</p> <p>Methods</p> <p>We systematically searched the literature using MEDLINE, EMBASE, Current Contents, the Cochrane Library, INAHTA, and the Web of Science to identify all primary prevention randomized controlled trials and prospective cohort studies with at least 12 months of follow-up examining MTWA as a predictor of mortality and severe arrhythmic events in patients with severe left-ventricular dysfunction. The search was limited to full-text English publications between January 1990 and May 2007. The primary outcome was a composite of mortality and severe arrhythmias. Data were synthesized using Bayesian hierarchical models.</p> <p>Results</p> <p>We identified no trials and 8 published cohort studies involving a total of 1,946 patients, including 332 positive, 656 negative, 84 indeterminate, and 874 non-negative (which includes both positive and indeterminate tests) MTWA test results. The risk of mortality or severe arrhythmic events was higher in patients with a positive MTWA compared to a negative test (RR = 2.7, 95% credible interval (CrI) = 1.4, 6.1). Similar results were obtained when comparing non-negative MTWA to a negative test.</p> <p>Conclusion</p> <p>A positive MTWA test predicts mortality or severe arrhythmic events in a population of individuals with severe left ventricular dysfunction. However, the wide credible interval suggests the clinical utility of this test remains incompletely defined, ranging from very modest to substantial. Additional high quality studies are required to better refine the role of MTWA in the decision making process for ICD implantation.</p