A Multistep Paroxysmal Atrial Fibrillation Scanning Strategy in Long-Term ECGs

Atrial fibrillation (AF) is a progressive disease often initially manifested by intermittent episodes spontaneously terminating and is an insidious disease. The previous work trained the support vector machine (SVM) classifier on multiple RR interval features. The trained AF detector was tested on the fourth China Physiological Signal Challenge (CPSC 2021) database, achieving 97.59% and 89.83% for sensitivity and specificity on dataset 1, respectively. The test results were 96.46% and 78.64% on dataset 2, respectively. The results show that the AF detector based on rhythm is insufficient for the recognition of non-AF. Therefore, this work developed AF scanning algorithm integrating rhythm and P-wave information for long-term ECGs. The proposed algorithm is divided into three steps. First, utilize a priori knowledge to locate suspected AF, and then, employ a trained rhythm-based AF detector to detect AF. Finally, adopt the dynamic time warping (DTW) and an autoencoding (AE) network to quantize the P-wave information to identify the non-AF signal from AF. The results on dataset 1 were 97.44% and 98.50% and on dataset 2 were 96.13% and 87.42%. This work scanned 11 patients with 24-h paroxysmal AF (PAF). The best result is that the detection accuracy is 99.33%, and the false detection is 0.01%, and the worst outcome is that the detection accuracy is 88.75%, and the false detection is 14.06%. The results proved that the proposed method could provide reliable scanning for PAF events.