VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation

TL;DR

VizECGNet is a novel deep learning model that classifies cardiovascular diseases using only printed ECG images by integrating multi-modal training and knowledge distillation, outperforming signal-based models.

Contribution

It introduces a multi-modal architecture with attention modules and knowledge distillation, enabling effective ECG classification solely from images in clinical settings.

Findings

Higher precision, recall, and F1-score compared to signal-based models.

Improved classification performance with 3.50% higher precision.

Effective use of printed ECG images during inference.

Abstract

An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digitized signals or printed images. Despite the emergence of numerous deep learning models for digitized signals, many hospitals prefer image storage due to cost considerations. Recognizing the unavailability of raw ECG signals in many clinical settings, we propose VizECGNet, which uses only printed ECG graphics to determine the prognosis of multiple cardiovascular diseases. During training, cross-modal attention modules (CMAM) are used to integrate information from two modalities - image and signal, while self-modality attention modules (SMAM) capture inherent long-range dependencies in ECG data of each modality. Additionally, we utilize knowledge distillation to improve the similarity between two distinct predictions from each modality stream. This innovative multi-modal deep learning architecture enables the utilization of only ECG images during inference. VizECGNet with image input achieves higher performance in precision, recall, and F1-Score compared to signal-based ECG classification models, with improvements of 3.50%, 8.21%, and 7.38%, respectively.