Anti-interference Ability Analysis for Common Atrial Fibrillation Features

Recently, a number of RR interval rhythm-based atrial fibrillation (AF) features have been developed and can achieve high classification accuracy for identifying AF from normal rhythm ECGs on clean signals. However, in dynamic ECG monitoring scenario, accurate location for QRS complexes is challenging, resulting in a deviation between calculated and reference RR interval sequences. This deviation can cause the failure of AF detection but its effect has not been quantified. This study addressed this concern and analyzed the anti-interference abilities of 14 commonly used AF features. Two types of Experiment were performed on the MIT-BIH AF database to simulate the deviation caused by QRS detection: 1) randomly moving forward or back several (0-15) labeled QRS locations to stimulate false detections, and 2) randomly missing several (0-9) labeled QRS locations to stimulate missing QRS detections. And the change in classification accuracy was regarded as a measure for evaluating the anti-interference ability of each feature. The results showed that features of AFEv, MAD, NFEn, COSEn and minRR showed high anti-interference abilities in both Experiments.