Learn More
The analysis and characterization of atrial tachyarrhythmias requires, in a previous step, the extraction of the atrial activity (AA) free from ventricular activity and other artefacts. This contribution adopts the blind source separation (BSS) approach to AA estimation from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA(More)
This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Lò eve transform is explained. Aspects on PCA related to data with temporal and spatial correlations are considered as adaptive estimation of principal(More)
A new method for the assessment of the atrial fibrillatory wave (AFW) from the ECG is presented. This methodology is suitable for signals registered from Holter systems, where the reduced number of leads is insufficient to exploit the spatial information of the ECG. The temporal dependence of the bio-electrical activity were exploited using principal(More)
In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have(More)
This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the(More)
INTRODUCTION Invasive high-density mapping of atrial fibrillation (AF) has revealed different patterns of atrial activation ranging from single wavefronts to disorganized activation with multiple simultaneous wavefronts. Whether or not similar activation patterns can also be observed using body surface recordings is currently unknown, and was consequently(More)
The analysis and characterization of atrial fibrillation requires the previous extraction of the atrial activity from the electrocardiogram, where the independent atrial and ventricular activities are combined in addition to noise. An independent component analysis method is proposed where additional knowledge about the time and statistical structure of the(More)
A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of(More)
BACKGROUND During atrial fibrillation (AF), RR interval histograms show different populations of predominant RR (pRR) intervals. These pRR intervals have been suggested to be multiples of the refractory period of the atrioventricular (AV) node or caused by the existence of a dual AV node physiology. In this study, the hypothesis that pRR intervals are(More)