Atrial fibrillation (AF) is known as the most common arrhythmia in adults, and it is estimated that millions of people suffer from AF. Previous studies reported that AF may increase risk for other diseases such as stroke, cardiovascular mortality, and obstructive sleep apnea.
Cardiac rhythms of patients with AF possess two particular characteristics: (i) R-R interval irregularity; and (ii) absence of P waves. Although the absence of P waves is a primary indicator of AF, P waves are low amplitude which makes them difficult to analyze due to high intensity noise in electrocardiograms (ECGs) derived from ambulatory electrocardiography. Compared to P waves, R waves have much higher amplitudes and therefore higher tolerance to ECG noise, making detection mush easier. Consequently, using R-R intervals to detect AF is considered to be the most robust approach.
Accordingly, several AF detection methods based on R-R interval irregularity have been proposed. However, the R-R interval window length required for those methods varies from 32 beats to 128 beats. To exhibit high accuracy and finer temporal resolution for real-time monitoring and shorter AF episodes detection, computational complexity together with the power consumption is dramatically increased.