Clustering of atrial fibrillation based on surface ECG measurements.

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical research. In particular, the study of AF types or sub-classes is a very interesting research topic. In this paper we present a preliminary study to find sub-classes of AF from real 12-lead ECG recordings using k-means and hierarchical clustering algorithms. We applied blind source separation to an initial set of 218 recordings from which we extracted a subset of 136 atrial activity signals displaying known properties of AF. As features for clustering we proposed the peak frequency mean value (PFM), peak frequency standard deviation (PFSD) and the spectral concentration (SC). We computed the silhouette coefficient to obtain an optimal number of clusters of k=5, and conducted preliminary feature selection to evaluate clustering quality. We observed that the separability increases if we discard SC as a feature. The proposed method is the first stage to a future AF classification method, which combined with specialist advice, should help in the clinical field.

Atrial activity selection for atrial fibrillation ECG recordings.

In this paper we apply independent component analysis (ICA) followed by second order blind identification (SOBI) to an atrial fibrillation (AF) 12-lead electrocardiogram (ECG) recording in order to extract the source that represents atrial activity (AA) (ICA-SOBI method). Still, there is no assurance that only one source obtained from this method will contain AA, and thus we aim to select the most representative source of AA. The novelty in this paper is the proposal of three parameters to select the most representative source of AA. These parameters are correlation coefficient with lead V1 (CV1), peak factor (PF) and spectral concentration (SC). The first two parameters are introduced as new indicators, addressing features overlooked by the SC even when they are present in AA during AF. For synthesized data, at least two of the three parameters select the same representation of AA in 93.3% of the cases. For real data (218 ECG recordings), we observe that PF presents, in 89.5% of the cases, values between 2 and 4.5 for the selected sources, ensuring a well-defined range of values for AA. The actual values of CV1 and SC were scattered throughout their possible ranges (0-1 for CV1 and 0.08-0.7 for SC), and the correlation coefficient between these variables was found to be ρ=0.58. We compared our results with three known algorithms: QRST cancellation, principal components analysis (PCA) and ICA-SOBI. The results obtained from this comparison show that our proposed methods to select the best representation of AA in general outperform the three above-mentioned algorithms.