RNN Based Deep Learning Approach for ECG Beat Classification

Abstract

The electrical patterns of the heart, captured through an electrocardiogram (ECG/EKG), serve as a diagnostic tool to identify potential issues such as heart attacks, irregular heart rhythms, heart failure, and arrhythmia, which manifests as irregularities in the heartbeat's rhythm. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This paper introduces a novel approach utilizing deep learning techniques, specifically a Bidirectional Gated Recurrent Unit (Bi-GRU) model a variant of RNN, to classify ECG arrhythmia beats into distinct categories. The Bi-GRU model is employed in this work due to its capability to capture temporal dependencies in ECG signals, enabling a nuanced understanding of beat sequences for precise classification. Leveraging the MIT-BIH arrhythmia database, a comprehensive dataset containing annotated ECG signals, this study explores the efficacy of deep learning in accurately categorizing beats in to five super classes as per the standard of Association for the Advancement of Medical Instrumentation (AAMI). Evaluation metrics encompassing accuracy (Acc), specificity (Spe), sensitivity (Sen), positive predictive value (Ppv), and F1-score are utilized to assess the model performance in distinguishing between diverse arrhythmia classes.