Atrial Fibrillation Prediction Using a Lightweight Temporal Convolutional and Selective State Space Architecture

TL;DR

This paper introduces a lightweight deep learning model combining TCN and a state space model for early prediction of atrial fibrillation using RR intervals, achieving high accuracy and efficiency, and capable of predicting AF up to two hours in advance.

Contribution

The study presents a novel, compact deep learning architecture that effectively predicts early-stage AF using minimal data and computational resources, outperforming traditional models.

Findings

Achieved sensitivity of 0.908 and specificity of 0.933.

Predicted AF up to two hours in advance with 30 minutes of data.

Model has only 73.5K parameters and 38.3 MFLOPs.

Abstract

Atrial fibrillation (AF) is the most common arrhythmia, increasing the risk of stroke, heart failure, and other cardiovascular complications. While AF detection algorithms perform well in identifying persistent AF, early-stage progression, such as paroxysmal AF (PAF), often goes undetected due to its sudden onset and short duration. However, undetected PAF can progress into sustained AF, increasing the risk of mortality and severe complications. Early prediction of AF offers an opportunity to reduce disease progression through preventive therapies, such as catecholamine-sparing agents or beta-blockers. In this study, we propose a lightweight deep learning model using only RR Intervals (RRIs), combining a Temporal Convolutional Network (TCN) for positional encoding with Mamba, a selective state space model, to enable early prediction of AF through efficient parallel sequence modeling. In subject-wise testing results, our model achieved a sensitivity of 0.908, specificity of 0.933, F1-score of 0.930, AUROC of 0.972, and AUPRC of 0.932. Additionally, our method demonstrates high computational efficiency, with only 73.5 thousand parameters and 38.3 MFLOPs, outperforming traditional Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) approaches in both accuracy and model compactness. Notably, the model can predict AF up to two hours in advance using just 30 minutes of input data, providing enough lead time for preventive interventions.