SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia screening. This study aimed to develop a reliable method that can improve the classification performance of atrial fibrillation (AF) using these devices based on the 2020 European Society of Cardiology (ESC) guidelines for AF diagnosis in clinical practice. We developed a deep learning model that utilizes RR interval frames for precise, beat-wise classification of electrocardiogram (ECG) signals. This model is specifically designed to sequentially classify each R peak on the ECG, considering the rhythms surrounding each beat. It features a two-stage bidirectional Recurrent Neural Network (RNN) with a many-to-many architecture, which is particularly optimized for processing sequential and time-series data. The structure aims to extract local features and capture long-term dependencies associated with AF. After inference, outputs which indicating either AF or non-AF, derived from various temporal sequences are combined through an ensembling technique to enhance prediction accuracy. We collected AF data from a clinical trial that utilized the MEMO Patch, an adhesive patch-type electrocardiograph. When trained on public databases, the model demonstrated high accuracy on the patch dataset (accuracy: 0.986, precision: 0.981, sensitivity: 0.979, specificity: 0.992, and F1 score: 0.98), maintaining consistent performance across public datasets. SeqAFNet was robust for AF classification, making it a potential tool in real-world applications.