  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.
