Evolutionary optimisation of atrial fibrillation diagnostic algorithms

This research proposes evolutionary optimisation for the improvement of atrial fibrillation (AF) detection. The basis of the AF detection algorithm is the irregularity of RR intervals (heartbeats) in the electrocardiogram (ECG) signal. We use three well-known statistical methods to detect RR interval irregularity: root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE). The Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) AF databases are used in conjunction with biogeography-based optimisation (BBO) to tune the parameters of the statistical methods. We trained each of the three statistical methods to diagnose AF on each MIT-BIH database. Then we tested each trained detection method on the rest of the MIT-BIH databases. The accuracy levels that were achieved for the detection of AF were as high as 99% in the trained databases, and up to 75% in the tested databases. Compared to previously published AF detection accuracies, the proposed tuning method achieved an improvement of 4% for RMSSD, 50% for SE, and 3% for TPR.