ECGEL: a multimodal 12-lead ECG classification model for heart failure prediction.

Abstract

Cardiovascular diseases (CVD) are the leading cause of death worldwide, with heart failure (HF) being one of the most fatal conditions within CVD, greatly impacting patients' quality of life and imposing a heavy socioeconomic burden. Early intervention can significantly reduce HF mortality and hospitalization rates. However, current diagnostic methods are often expensive and complex, leading to delayed detection. To address this issue, this paper proposes a multimodal model, ECGEL, which combines electrocardiogram (ECG) and clinical text data for heart failure prediction. The model first denoises 12-lead ECG signals using LUNet, then converts the ECG signals into spectrograms via fast Fourier transform, extracting ECG features using EfficientNetv2. Simultaneously, clinical text is preprocessed with Bert, and textual features are extracted using BiLSTM. Finally, the ECG and text features are fused for heart failure prediction. Experimental results show that the ECGEL model achieved outstanding performance on a private dataset, with accuracy of 97.9%, recall of 98.3%, and F1 score of 97.6%. This model offers an efficient and accurate solution for the early diagnosis of heart failure, showing significant potential for clinical application.