Novel Rank-based Features of Atrial Potentials for the Classification Between Paroxysmal and Persistent Atrial Fibrillation
Hanie Moghaddasi (TU Delft - Signal Processing Systems)
R. C. Hendriks (TU Delft - Signal Processing Systems)
AJ van der Veen (TU Delft - Signal Processing Systems)
Natasja M.S. de Groot (Erasmus MC, TU Delft - Biomechanical Engineering, TU Delft - Signal Processing Systems)
Borbala Hunyadi (TU Delft - Signal Processing Systems)
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Abstract
Atrial fibrillation (AF) is the most common arrhythmia. Although the exact cause is unclear, electropathology of atrial tissue is one contributing factor. Electropathological characteristics derived from intra-operative epicardial measurements, such as conduction block (CB) and continues conduction delay and block (cCDCB), can be used to assess the severity of AF. In sinus rhythm, however, these parameters do not indicate significant difference between different development stages of AF, such as paroxysmal and persistent AF. Therefore, we propose a methodology to improve AF severity detection using intra-operative electrograms. We propose a model that describes the spatial diversity of atrial potential waveforms during a single beat on the multi-channel electrograms. Based on this model, we derive two novel features. During sinus rhythm, we used 293 beats from patients with a history of paroxysmal or persistent AF. Using a random forest classifier, we achieved 78.42% classification accuracy, while classification based on the CB and cCDCB leads to an accuracy of 58.34%.
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