Spontaneous termination of atrial fibrillation: a challenge from physionet and computers in cardiology 2004

“…To test the methods on real ECG recordings, we considered the data coming from the 2004 Computers in Cardiology AF Termination Challenge Database [16]. The Database includes 80 records extracted from long-term ECG Holter of patients undergoing AF episodes.…”

Section: Af Databasementioning

confidence: 99%

Analysis of surface atrial signals using spectral methods for time series with missing data

Sassi¹,

Corino

Mainardi

2007

2007 Computers in Cardiology

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In this work, the analysis of atrial signals recorded during atrial fibrillation was pursued using two spectral estimators designed for series with missing data: the Lomb periodogram (LP) and the Iterative Singular Spectrum Analysis (ISSA). The main aim is to verify if subtraction of the ventricular activity might be avoided by performing spectral analysis on those ECG intervals where such activity is absent, (i.e. the T-Q intervals), at least to estimate the dominant atrial Fibrillatory Frequency (FF).Recordings coming from the 2004 Computers in Cardiology Termination Challenge Database were analyzed. Fibrillatory frequencies were then compared with those obtained from the analysis of the correspondent atrial signals extracted using a modified Average Beat Substraction (ABS) technique. We observed that the mean absolute difference was 0.42 ± 0.66 Hz for LP, (mean±SD), and 0.39 ± 0.64 Hz for ISSA. We concluded that estimation of FF is feasible without applying QRS-T subtraction.

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“…It is particularly important the understanding of the mechanisms of spontaneous termination of PAF episodes because it could lead to improvements in treatment of sustained AF [21]. …”

Section: Epidemiologymentioning

confidence: 99%

“…Once the AA is obtained with enough accuracy from ECG recordings, it can be used for the characterization of PAF episodes by signal analysis. Recognizing the conditions under which PAF is likely to self-terminate might help to choose the most suitable intervention in affected individuals [21] and to refrain from applying unnecessary therapies [20]. The analysis proposed in this thesis entails studying the regularity of certain spectral features of the AA source in the time-frequency domain.…”

Section: Objectivesmentioning

confidence: 99%

“…The PhysioNet/Computers in Cardiology Challenge 2004 [21] marked the start of continuous attempts to predict the early termination of PAF episodes by using the electrocardiogram (ECG). Several groups based their study on the atrial activity (AA) overall peak frequency plus additional spectral features as the main peak power [22] or time-frequency pattern [23].…”

Section: Agradecimientosmentioning

confidence: 99%

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Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

Salort¹

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Supraventricular tachyarrhythmias, in particular Atrial Fibrillation (AF), are the most commonly cardiac diseases encountered in the routine clinical practice. The prevalence of AF is less than 1% among population under 60 years old, but it increases significantly among those over 70, approximating to 10% in those older than 80. Undergoing a sutained AF episode is related to a higher mortality ratio and to a rising probability of suffering thromboembolisms, myocardial infarction, and stroke. On the other hand, paroxysmal AF (PAF), which is characterized by its spontaneous termination, is frequently the precursor to sustained AF. This provokes a great interest among the scientific community in disclosing the mechanisms which lead to AF perpetuation or to spontaneous AF termination.The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of cardiac pathologies. In order to use the ECG as a tool for the AF study, the atrial activity (AA) must be separated from other cardioelectric signals. In this sense, Blind Source Separation (BSS) techniques are able to perform a multi-lead statistical analysis with the aim to obtain a set of independent sources that include the AA. When the BSS problem is tackled, it becomes necessary to consider a source mixing model near to the real mixing process in order to develop mathematical algorithms that solve the problem. A feasible model consists of assuming the linear mixture of sources. Within this linear mixing model it can be made the additional assumption of instantaneous mixture. This instantaneous linear mixing model is the one used in Independent Component Analysis (ICA). An alternative mixing model is considered by convolutive BSS (CBSS) algorithms, where a more realistic process in the generation of ECG leads is taken into account with delayed contributions of cardioelectric sources.In this thesis, a performance study of CBSS algorithms applied to AA extraction from ECG recordings has been carried out for the first time. With this aim, the most relevant CBSS algorithms have been compared with the instantaneous algorithm FastICA, the effectivity of which is extensively proved. This comparison will allow to know which CBSS algorithms are useful for AA extraction from ECG recordings of AF episodes. On the other hand, CBSS algorithms have the problem of requiring a minimum number of observed signals for their suitable vi ABSTRACT application. Here a new AA extraction algorithm is presented, which is based on the convolutive mixing model and solves the problem of lack of available leads from Holter ECG recordings. The high likeliness level between original and estimated AA, measured by different performance indicators, demonstrates the suitability of this new method for the AA extraction from Holter recordings and, furthermore, a higher robustness against noise of the convolutive mixing model is highlighted.The most common cause of undergoing an AF episode is attributed to the reentry mechanism, which consists o...

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Spontaneous termination of atrial fibrillation: a challenge from physionet and computers in cardiology 2004

Abstract: The fifth annual PhysioNet

Cited by 70 publications

References 2 publications

Analysis of scaling behaviour of ECG signal during atrial fibrillation

Analysis of scaling behaviour of ECG signal during atrial fibrillation

Analysis of surface atrial signals using spectral methods for time series with missing data

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

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