A New ML-based AFIB Detector

Abstract

Objectives: This paper aims to develop a model for detecting Atrial Fibrillation (AFIB) in a single-channel electrocardiogram.Methodology: The applied Machine Learning methods are XGBoost and Random Forest. Training and testing split was realized by splitting the patients 70% for training and 30% for testing. Features included annotations for heartbeats, intervals between neighboring heartbeats, Shannon entropy, and Fluctuation index by designing a moving window with predefined length.Data: Standard ECG benchmarks were used for training and testing from MIT-BIH Arrhythmia [1] and MIT-BIH Atrial Fibrillation[2] datasets.Conclusion: Experiments were done with different window sizes and different hyperparameters. The best results were achieved from the 41 Beat window, with XGBoost achieving the best performance of 99% with an F1 score of 99%.