Francisco Castells

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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)
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)
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)
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)
Discrimination between normal sinus rhythm (NSR), Atrial Fibrillation (AF) and Atrial Flutter (AFL) using the surface electrocardiogram (ECG) through direct Fourier analysis is not effective because ventricular activity (VA) overlaps in frequency with atrial activity (AA) and, moreover, AA frequency components present considerably lower amplitude than VA.(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)
Detection of atrial activity (AA) is quite important in the study and monitoring of atrial rhythms, in particular atrial flutter and atrial fibrillation (FA). An efficient non-invasive study of the AA needs the ventricular activity cancellation. The Discrete Packet Wavelet Transform (DPWT) allows the decomposition of the original ECG in a set of(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)
In this work we present a new biomedical application of independent component analysis (ICA) to solve the problem of atrial activity (AA) extraction from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). The proper analysis and characterization of AA from ECG recordings requires, as a first step, the cancellation of ventricular activity(More)