Arcadi García-Alberola

Brugada-like electrocardiographic pattern induced by fever.

SAURA, D., et al.: Brugada‐Like Electrocardiographic Pattern Induced by Fever. The Brugada syndrome is characterized by a peculiar ST‐segment elevation in the right precordial leads and the propensity to develop ventricular arrhythmias. Mutations in a cardiac sodium channel gene have been linked to this syndrome and some experimental data suggest that the dysfunction of the mutated channel can be temperature sensitive. This report describes a patient in whom a typical Brugada ECG pattern developed in relation to fever but could not be reproduced at normal temperature on administration of flecainide. This case suggests that in some patients a Brugada‐like ECG may only manifest during a febrile state.

Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines

Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each parameter individually. In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously proposed ECG parameters by using support vector machines classifiers. A total of 13 parameters were computed accounting for temporal (morphological), spectral, and complexity features of the ECG signal. A filter-type feature selection (FS) procedure was proposed to analyze the relevance of the computed parameters and how they affect the detection performance. The proposed methodology was evaluated in two different binary detection scenarios: shockable (FV plus VT) versus nonshockable arrhythmias, and VF versus nonVF rhythms, using the information contained in the medical imaging technology database, the Creighton University ventricular tachycardia database, and the ventricular arrhythmia database. sensitivity (SE) and specificity (SP) analysis on the out of sample test data showed values of SE=95%, SP=99%, and SE=92% , SP=97% in the case of shockable and VF scenarios, respectively. Our algorithm was benchmarked against individual detection schemes, significantly improving their performance. Our results demonstrate that the combination of ECG parameters using statistical learning algorithms improves the efficiency for the detection of life-threatening arrhythmias.

Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection

Early detection of ventricular fibrillation (VF) is crucial for the success of the defibrillation therapy in automatic devices. A high number of detectors have been proposed based on temporal, spectral, and time-frequency parameters extracted from the surface electrocardiogram (ECG), showing always a limited performance. The combination ECG parameters on different domain (time, frequency, and time-frequency) using machine learning algorithms has been used to improve detection efficiency. However, the potential utilization of a wide number of parameters benefiting machine learning schemes has raised the need of efficient feature selection (FS) procedures. In this study, we propose a novel FS algorithm based on support vector machines (SVM) classifiers and bootstrap resampling (BR) techniques. We define a backward FS procedure that relies on evaluating changes in SVM performance when removing features from the input space. This evaluation is achieved according to a nonparametric statistic based on BR. After simulation studies, we benchmark the performance of our FS algorithm in AHA and MIT-BIH ECG databases. Our results show that the proposed FS algorithm outperforms the recursive feature elimination method in synthetic examples, and that the VF detector performance improves with the reduced feature set.

Penetrance and risk profile in inherited cardiac diseases studied in a dedicated screening clinic.

Genetically transmitted cardiomyopathies can affect several members in a family. Identification of high-risk patients could lead to a preventive treatment. We report the results of a 5-year experience of a dedicated clinic. Family screening was offered to 493 consecutive unrelated patients; 2,328 subjects (40 +/- 19 years old, 52% men) were evaluated (mean 4.4 relatives/family). Electrocardiography and echocardiography were performed in all cases; additional tests were indicated depending on the disease. Familial study was recommended because of a proband with hypertrophic cardiomyopathy (HC) in 57%, idiopathic dilated cardiomyopathy (IDC) in 14%, arrhythmogenic right ventricular cardiomyopathy (ARVC) in 2%, left ventricular noncompaction in 2%, Brugada syndrome (BS) in 15%, long QT syndrome (LQTS) in 3%, and other conditions in 6%. Familial disease was confirmed in 164 (39%); 43% with HC, 47% with IDC, 25% with ARVC, 33% with left ventricular noncompaction, 28% with BS, and 30% with LQTS. Two hundred twenty-two (44 +/- 20 years old, 60% men) affected relatives were identified (129 of whom were newly diagnosed). Sixty-four patients were newly diagnosed with HC, 40 with IDC, 2 with ARVC, 5 with left ventricular noncompaction, 14 with BS, and 2 with LQTS, in whom appropriate risk stratification and medication, if needed, were initiated (specific medication in 40, 31.0%). Cardioverter-defibrillator implantation was indicated in 4 relatives for primary prevention. Ninety-two (18.7%) had a family history of sudden death (FHSCD). Consanguinity was rare but significantly associated to a higher percentage of family disease (75.0% vs 38.3%, p = 0.003) and family history of sudden death (42.1% vs 17.8, p <0.001). In conclusion, the prevalence of familial disease in inherited cardiac conditions is high. Systematic familial study identified many asymptomatic affected patients who could benefit from early treatment to prevent complications. Dedicated clinics and multidisciplinary teams are needed for proper screening programs.

Heart Rate Turbulence Analysis Based on Photoplethysmography

The goal of this paper is to determine whether the photoplethysmography (PPG) can replace the ECG-based detection of heart rate turbulence. Using the PPG, classification of ventricular premature beats (VPBs) is accomplished with a linear classifier. The two conventional parameters turbulence onset and slope are studied together with a recently introduced parameter characterizing turbulence shape. Performance is studied on a dataset with 4131 VPBs, recorded from a total of 27 patients in different clinical contexts (hemodialysis treatment, intensive care monitoring, and electrophysiological study). The sensitivity/specificity of VPB classification was found to be 90.5/99.9%, with an accuracy of 99.3%, suggesting that classification of VPBs can be reliable made from the PPG. The main difference between the two types of turbulence analysis stems from the fact that the pulse transit time varies largely immediately after the VPB. Out of the 22 patients which had a sufficient number of VPBs, the outcome of the ECG- and PPG-based analysis was identical in 21. It is concluded that the PPG may serve as a surrogate technique for the ECG in turbulence analysis.

Heart Rate Turbulence Denoising Using Support Vector Machines

Heart rate turbulence (HRT) is a transient acceleration and subsequent deceleration of the heart rate after a premature ventricular complex (PVC), and it has been shown to be a strong risk stratification criterion in patients with cardiac disease. In order to reduce the noise level of the HRT signal, conventional measurements of HRT use a patient-averaged template of post-PVC tachogram (PPT), hence providing with long-term HRT indexes. We hypothesize that the reduction of the noise level at each isolated PPT, using signal processing techniques, will allow us to estimate short-term HRT indexes. Accordingly, its application could be extended to patients with reduced number of available PPT. In this paper, several HRT denoising procedures are proposed and tested, with special attention to support vector machine (SVM) estimation, as this is a robust algorithm that allows us to deal with few available time samples in the PPT. Pacing-stimulated HRT during electrophysiological study are used as a low-noise gold standard. Measurements in a 24-h Holter patient database reveal a significant reduction in the bias and the variance of HRT measurements. We conclude that SVM denoising yields short-term HRT measurements and improves the signal-to-noise level of long-term HRT measurements.

Heart Rate Variability on 7-Day Holter Monitoring Using a Bootstrap Rhythmometric Procedure

Heart rate variability (HRV) markers have been widely used to characterize the autonomous regulation state of the heart from 24-h Holter monitoring, but long-term evolution of HRV indexes is mostly unknown. A dataset of 7-day Holter recordings of 22 patients with congestive heart failure was studied. A rhythmometric procedure was designed to characterize the infradian, circadian, and ultradian components for each patient, as well as circadian and ultradian fluctuations. Furthermore, a bootstrap test yielded automatically the rhythmometric model for each patient. We analyzed the temporal evolution of relevant time-domain (AVNN, SDNN, and NN50), frequency-domain (LF, HF, HFn, and LF/HF), and nonlinear (α1 and SampEn) HRV indexes. Circadian components were the most significant for all HRV indexes, but the infradian ones were also strongly present in NN50, HFn, LF/HF, α1, and SampEn indexes. Among ultradian components that one corresponding to 12 h, was the most relevant. Long-term monitoring of HRV conveys new potentially relevant rhythmometric information, which can be analyzed by using the proposed automatic procedure.

Fundamental Frequency and Regularity of Cardiac Electrograms With Fourier Organization Analysis

Dominant frequency analysis (DFA) and organization analysis (OA) of cardiac electrograms (EGMs) aims to establish clinical targets for cardiac arrhythmia ablation. However, these previous spectral descriptions of the EGM have often discarded relevant information in the spectrum, such as the harmonic structure or the spectral envelope. We propose a fully automated algorithm for estimating the spectral features in EGM recordings This approach, called Fourier OA (FOA), accounts jointly for the organization and periodicity in the EGM, in terms of the fundamental frequency instead of dominant frequency. In order to compare the performance of FOA and DFA-OA approaches, we analyzed simulated EGM, obtained in a computer model, as well as two databases of implantable defibrillator-stored EGM. FOA parameters improved the organization measurements with respect to OA, and averaged cycle length and regularity indexes were more accurate when related to the fundamental (instead of dominant) frequency, as estimated by the algorithm (p <; 0.05 comparing f0 estimated by DFA and by FOA). FOA yields a more detailed and robust spectral description of EGM compared to DFA and OA parameters.

Sensitivity and Spatial Resolution of Transvenous Leads in Implantable Cardioverter Defibrillator

It has been previously documented that the main features and sensing performance of electrograms (EGMs) recorded in implantable cardioverter defibrillators (ICDs) depend on lead configuration. Although this dependence has been ascribed to differences in lead sensitivity and spatial resolution, the quantification of these two properties on ICD has not yet been attempted. In this paper, an operative framework to study the spatial resolution of ICD transvenous leads is presented. We propose to quantify the spatial resolution of ICD transvenous leads based on a new characterization called lead resolution volume (ResV). We analyzed the sensitivity distribution and the ResV of two unipolar (tip-can and coil-can ) and two bipolar (true or tip-ring and integrated or tip-coil) ICD transvenous lead configurations. A detailed 3-D model of the human thorax based on the visible human man dataset was used to compute the lead sensitivity and computer simulations of simple cardiac dynamics were used to quantify the ResV. Differences in the sensitivity distribution throughout the ventricular myocardium (VM) were observed for each lead configuration. In our computer model of the human thorax, the ResV was found to comprise 7%, 35%, 45%, and 70% of the VM for true bipolar, integrated bipolar, tip-can unipolar, and coil-can unipolar ICD leads, respectively. Furthermore, our analysis shows that the spatial resolution depends on both lead sensitivity and cardiac dynamics, and therefore, it can vary for different heart rhythms.

Shape reconstruction of cardiac ischemia from non-contact intracardiac recordings: A model study

Abstract We present a new approach for the reconstruction of ischemic regions from only a few non-contact intracardiac recordings. Hence, it is desirable to exploit the spatio-temporal correlations contained in the data. To this end, we incorporate a time-dependent monodomain model of the cardiac electric activity into the inversion scheme. In order to take into account the electrophysiological alterations of ischemic regions, we also introduce appropriate variations of the model parameters. This approach allows us to perform the reconstruction of the affected regions successfully using only a few recording sites. The reconstruction process is based on level set techniques. Our numerical experiments in a bi-dimensional model of cardiac tissue validate our approach.