A Comprehensive Deep Learning Pipeline for Arrhythmia Multi-Classification with Electrocardiography Data

Abstract Accurate and timely arrhythmia multi-classification is crucial for diagnosing and managing cardiovascular diseases. Traditional interpretation of electrocardiograms is time-consuming and prone to human error. To address these challenges, this study describes a novel deep learning-based approach for automated arrhythmia classification. Our proposed model, a convolutional-recurrent neural network, leverages the strengths of both convolutional and recurrent neural networks to effectively capture spatial and temporal features within electrocardiography signals. To outperform the existent deep learning solutions, we incorporate a systematic data balancing strategy to address the class imbalance often present in the available datasets. Furthermore, we employ an automatic hyperparameter optimization technique to fine-tune the model’s parameters for optimal performance. The proposed deep learning architecture, trained on a robust dataset of electrocardiograms, demonstrates exceptional accuracy in classifying multiple arrhythmia types. Our results, with an overall accuracy of 99.60%, surpass previous state-of-the-art methods, highlighting the potential of our approach to improve the efficiency and accuracy of arrhythmia diagnosis in clinical settings.