Atrial fibrillation (AFib) is a sustained cardiac arrythmia and is classified according to the temporal pattern of irregularly spaced heart beats. Patients with AFib have cardiac hemodynamic dysfunction, have up to 2-fold increase in risk of mortality, and a 6-fold increase in risk of stroke. The electrocardiographic presentation of AFib is continuous and rapid irregular electrical activity of the atria and absence of the P-wave because ventricular response is poorly coupled with atrial activity. These hallmark characteristic of AFib make ECG monitoring the most convenient tool to assist AFib diagnosis. Automated algorithms rely on one or more characteristics of the waveform including irregular rhythm, high-frequency chaotic atrial waveform, and absence of P waves. Measures of heart rate variability (HRV) and morphological analysis are the most common approaches.

R-R interval

We can use a beat detection algorithm to find the R-peaks of the QRS complexes, which are recorded as indices \(R_{peaks}[n]\) for the \(n\)-th beat. We can convert the indices to an R-R interval (RRI) in units of seconds by taking the first difference and dividing by the sampling rate \(f_s\):

\[RRI[n] = \frac{R_{peaks}[n] - R_{peaks}[n-1]}{f_s}\]

Beat detection

Poincare plots

A Poincare plot is a diagram in which the RR-interval (\(RRI[n]\)) is plotted as a function of the previous RR-interval (\(RRI[n-1]\)) and is a visual representation of heart rate variability. Patients with AFib have irregular RRI and the dispersion is wider. As such, we can quantify this dispersion by fitting an ellipsis and measuring the standard deviation along the major and minor axes. The area of the fitted ellipsis is also larger in AFib patients.