A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. Existing algorithms can only distinguish ``AF rhythm in AF patients'' from ``sinus rhythm in normal individuals'' . However, AF patients do not always exhibit AF rhythm, posing a challenge for diagnosis when the AF rhythm is absent. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish ``sinus rhythm in AF patients'' and ``sinus rhythm in normal individuals'' in beat-level. We introduce beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels.