Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

Optical imaging of voltage and calcium in isolated hearts: Linking spatiotemporal heterogeneities and ventricular fibrillation initiation.

BACKGROUND: Alternans have been associated with the development of ventricular fibrillation and its control has been proposed as antiarrhythmic strategy. However, cardiac arrhythmias are a spatiotemporal phenomenon in which multiple factors are involved (e.g. calcium and voltage spatial alternans or heterogeneous conduction velocity) and how an antiarrhythmic drug modifies these factors is poorly understood. OBJECTIVE: The objective of the present study is to evaluate the relation between spatial electrophysiological properties (i.e. spatial discordant alternans and conduction velocity) and the induction of ventricular fibrillation (VF) when a calcium blocker is applied. METHODS: The mechanisms of initiation of VF were studied by simultaneous epicardial voltage and calcium optical mapping in isolated rabbit hearts using an incremental fast pacing protocol. The additional value of analyzing spatial phenomena in the generation of unidirectional blocks and reentries as precursors of VF was depicted. Specifically, the role of action potential duration (APD), calcium transients (CaT), spatial alternans and conduction velocity in the initiation of VF was evaluated during basal conditions and after the administration of verapamil. RESULTS: Our results enhance the relation between (1) calcium spatial alternans and (2) slow conduction velocities with the dynamic creation of unidirectional blocks that allowed the induction of VF. In fact, the administration of verapamil demonstrated that calcium and not voltage spatial alternans were the main responsible for VF induction. CONCLUSIONS: VF induction at high activation rates was linked with the concurrence of a low conduction velocity and high magnitude of calcium alternans, but not necessarily related with increases of APD. Verapamil can postpone the development of cardiac alternans and the apparition of ventricular arrhythmias.

Clinical Characteristics and Electrophysiological Mechanisms Underlying Brugada ECG in Patients With Severe Hyperkalemia.

Background Several metabolic conditions can cause the Brugada ECG pattern, also called Brugada phenotype (BrPh). We aimed to define the clinical characteristics and outcome of BrPh patients and elucidate the mechanisms underlying BrPh attributed to hyperkalemia. Methods and Results We prospectively identified patients hospitalized with severe hyperkalemia and ECG diagnosis of BrPh and compared their clinical characteristics and outcome with patients with hyperkalemia but no BrPh ECG. Computer simulations investigated the roles of extracellular potassium increase, fibrosis at the right ventricular outflow tract, and epicardial/endocardial gradients in transient outward current. Over a 6-year period, 15 patients presented severe hyperkalemia with BrPh ECG that was transient and disappeared after normalization of their serum potassium. Most patients were admitted because of various severe medical conditions causing hyperkalemia. Six (40%) patients presented malignant arrhythmias and 6 died during admission. Multiple logistic regression analysis revealed that higher serum potassium levels (odds ratio, 15.8; 95% CI, 3.1-79; P=0.001) and male sex (odds ratio, 17; 95% CI, 1.05-286; P=0.045) were risk factors for developing BrPh ECG in patients with severe hyperkalemia. In simulations, hyperkalemia yielded BrPh by promoting delayed and heterogeneous right ventricular outflow tract activation attributed to elevation of resting potential, reduced availability of inward sodium channel conductance, and increased right ventricular outflow tract fibrosis. An elevated transient outward current gradient contributed to, but was not essential for, the BrPh phenotype. Conclusions In patients with severe hyperkalemia, a BrPh ECG is associated with malignant arrhythmias and all-cause mortality secondary to resting potential depolarization, reduced sodium current availability, and fibrosis at the right ventricular outflow tract.

On the Influence of Heart Rate and Coupling Interval Prematurity on Heart Rate Turbulence.

OBJECTIVE: Heart rate turbulence (HRT) has been successfully explored for cardiac risk stratification. While HRT is known to be influenced by the heart rate (HR) and the coupling interval (CI), nonconcordant results have been reported on how the CI influences HRT. The purpose of this study is to investigate HRT changes in terms of CI and HR by means of an especially designed protocol. METHODS: A dataset was acquired from 11 patients with structurally normal hearts for which CI was altered by different pacing trains and HR by isoproterenol during electrophysiological study (EPS). The protocol was designed so that, first, the effect of HR changes on HRT and, second, the combined effect of HR and CI could be explored. As a complement to the EPS dataset, a database of 24-h Holters from 61 acute myocardial infarction (AMI) patients was studied for the purpose of assessing risk. Data analysis was performed by using different nonlinear ridge regression models, and the relevance of model variables was assessed using resampling methods. The EPS subjects, with and without isoproterenol, were analyzed separately. RESULTS: The proposed nonlinear regression models were found to account for the influence of HR and CI on HRT, both in patients undergoing EPS without isoproterenol and in low-risk AMI patients, whereas this influence was absent in high-risk AMI patients. Moreover, model coefficients related to CI were not statistically significant, p > 0.05, on EPS subjects with isoproterenol. CONCLUSION: The observed relationship between CI and HRT, being in agreement with the baroreflex hypothesis, was statistically significant ( ), when decoupling the effect of HR and normalizing the CI by the HR. SIGNIFICANCE: The results of this study can help to provide new risk indicators that take into account physiological influence on HRT, as well as to model how this influence changes in different cardiac conditions.

Corrigendum: Regularization Techniques for ECG Imaging during Atrial Fibrillation: A Computational Study.

[This corrects the article on p. 466 in vol. 7, PMID: 27790158.].

Regularization Techniques for ECG Imaging during Atrial Fibrillation: A Computational Study.

The inverse problem of electrocardiography is usually analyzed during stationary rhythms. However, the performance of the regularization methods under fibrillatory conditions has not been fully studied. In this work, we assessed different regularization techniques during atrial fibrillation (AF) for estimating four target parameters, namely, epicardial potentials, dominant frequency (DF), phase maps, and singularity point (SP) location. We use a realistic mathematical model of atria and torso anatomy with three different electrical activity patterns (i.e., sinus rhythm, simple AF, and complex AF). Body surface potentials (BSP) were simulated using Boundary Element Method and corrupted with white Gaussian noise of different powers. Noisy BSPs were used to obtain the epicardial potentials on the atrial surface, using 14 different regularization techniques. DF, phase maps, and SP location were computed from estimated epicardial potentials. Inverse solutions were evaluated using a set of performance metrics adapted to each clinical target. For the case of SP location, an assessment methodology based on the spatial mass function of the SP location, and four spatial error metrics was proposed. The role of the regularization parameter for Tikhonov-based methods, and the effect of noise level and imperfections in the knowledge of the transfer matrix were also addressed. Results showed that the Bayes maximum-a-posteriori method clearly outperforms the rest of the techniques but requires a priori information about the epicardial potentials. Among the purely non-invasive techniques, Tikhonov-based methods performed as well as more complex techniques in realistic fibrillatory conditions, with a slight gain between 0.02 and 0.2 in terms of the correlation coefficient. Also, the use of a constant regularization parameter may be advisable since the performance was similar to that obtained with a variable parameter (indeed there was no difference for the zero-order Tikhonov method in complex fibrillatory conditions). Regarding the different targets, DF and SP location estimation were more robust with respect to pattern complexity and noise, and most algorithms provided a reasonable estimation of these parameters, even when the epicardial potentials estimation was inaccurate. Finally, the proposed evaluation procedure and metrics represent a suitable framework for techniques benchmarking and provide useful insights for the clinical practice.

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.