The ability of photoplethysmography (PPG)-based wearable monitors to diagnose atrial ?brillation (AF) is still partly unexplored. This study aims at quantifying the level of organization of PPG signals, using a measure directly based on the PPG waveforms, during electrophysiology procedures and to assess its potential to characterize AF episodes. The database includes 18 adult patients undergoing catheter ablation of cardiac arrhythmias. PPG signals were recorded using a wrist-type sensor. A 12-lead ECG was used as gold standard. ECGs were annotated by experts and selected segments were divided into 4 categories: sinus rhythm (SR), regularly paced rhythm (RPR), irregularly paced rhythm (IPR) and AF. The PPG adaptive organization index (AOI), de?ned as the ratio of the power of the fundamental frequency and the ?rst harmonic to the total power of the PPG signal, was computed using adaptive band-pass ?lters. A total of 2806/803/852/287 10-second epochs were considered for AF/SR/RPR/IPR classes. The following mean AOI values were measured: 0.45±0.11 for AF, 0.73±0.19 for SR, 0.78±0.20 for RPR and 0.61±0.19 for IPR classes. Importantly, the AF AOI was signi?cantly smaller than that of the other categories (p<0.001), indicating a higher degree of disorganization.
2016 Computing in Cardiology Conference, Vancouver, BC (Canada)