An improved Poincare plot-based method to detect atrial fibrillation from short single-lead ECG

Purpose: With the popularity of wearable mobile devices, increasing numbers of applications use single-lead electrocardiograms (ECGs), which provides information about heart rate and rhythm to detect cardiac diseases. Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, it is very important to detect AF early. In this study, we propose a Poincare plot-based method for AF detection that has the advantages of requiring few calculations and short detection segments. Method: Our method first extracts the first-order differential RR interval (Delta RRI) sequences from a segmented ECG data, and then the polar coordinate transformation is performed on the Poincare plot of the Delta RRI to obtain the phase-based distribution. Two features, the distribution width D-w and the average distribution height D-h are extracted from the phase-based distribution to classify the AF and non AF episodes. Five PhysioNet databases were used for the assessment, which contain 3,843.3 hours of data from 229 subjects. Long Term AF Database was selected as the training set, and the remaining four databases were used as the testing sets. Results: In the testing sets, the results yield good sensitivity/specificity (97.91%/99.14%) with 60 second ECG signal segments, which are better than those of the existing the Poincare plot-based methods, and the method also performs well with 20 second ECG signal segments (sensitivity/specificity of 97.28%/98.35%). Conclusion: The detection performance of this method is good under different condition, and has the advantage of easy implementation, which makes it promising for application in the detection and monitoring of AF.

Automated summary

This study introduces a Poincare plot-based method for atrial fibrillation (AF) detection, which effectively extracts features and classifies AF episodes in a short period of time with high sensitivity and specificity. The method shows promise for wide application in the detection and monitoring of AF.