Extracting atrial activations from intracardiac signals during atrial fibrillation using adaptive mathematical morphology

The detection of intracardiac activities is a major issue in the processing of atrial fibrillation signals. we evaluate a method based on mathematical morphology with an adaptive structuring element in order to extract the atrial activations from intracardiac electrograms. The structuring element is continuously updated for each activation based on the morphological characteristics of the previously detected activations. A dataset of recordings from patients with chronic atrial fibrillation who underwent catheter ablation were used in order to evaluate the performance of the proposed method. Results show high performance compared to a dataset manually annotated by an expert. The detection rate, sensitivity and positive prediction value (PPV) were respectively 99.1%, 99.5%, 99.5%. The proposed method is fast and offers low computational cost, which makes it a suitable approach for real-time/online scenarios.