A novel sparsity-aware learning technique improves the spectral analysis of atrial fibrillation electrograms by accounting for multiple uncoordinated activation foci.
May enhance AF electrogram research tools; leaves open clinical utility pending prospective validation.
Atrial fibrillation (AF) is a common heart disorder. One of the most prominent hypothesis about its initiation and maintenance considers multiple uncoordinated activation foci inside the atrium. However, the implicit assumption behind all the signal processing techniques used for AF, such as dominant frequency and organization analysis, is the existence of a single regular component in the observed signals. In this paper we take into account the existence of multiple foci, performing a spectral analysis to detect their number and frequencies. In order to obtain a cleaner signal on which the spectral analysis can be performed, we introduce sparsity-aware learning techniques to infer the spike trains corresponding to the activations. The good performance of the proposed algorithm is demonstrated both on synthetic and real data.
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Monzón et al. (2012) studied this question.
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