CardioBERT: A Cardiac Identification Using Fusion Features in Consumer Healthcare

Abstract

Electrocardiogram (ECG) readings play a vital role in diagnosing cardiovascular diseases, including myocardial infarction (MI), a condition that severely damages heart tissue and can lead to fatal outcomes. Consumer electronic devices are used to collect ECG signals, which reveal crucial details about MI. Timely and precise diagnosis is essential to reduce mortality, and this can be enhanced using advanced deep-learning models like ResNet. This paper introduces CardioBERT, designed to detect cardiovascular disease from ECG signals using Convolutional Neural Network (CNN) and large language models (LLMs) like BERT. Since CNN is traditionally built for multidimensional data, whereas ECG signals are inherently one-dimensional, our CardioBERT employs residue-level contact-map predictions to extract and optimally integrate features, effectively addressing the dimensionality mismatch. Furthermore, BERT enriches the feature fusion process by capturing and interpreting intricate patterns within the data. By employing consumer electronics and mathematical transformations (e.g., reciprocal and cubic functions), the CardioBERT achieves a notable 0.92% increase in accuracy with existing methods. This improvement underscores the potential of our CardioBERT, enhanced by LLMs, to advance cardiovascular healthcare systems significantly.