EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification

A new geometric-stochastic multimodal deep learning framework has been developed to predict vulnerability to Sudden Unexpected Death in Epilepsy (SUDEP) and acute ischemic stroke, integrating various physiological signals such as EEG, ECG, and fMRI. This approach utilizes advanced mathematical models to enhance predictive accuracy and interpretability of biomarkers derived from complex brain dynamics.

A new method called Pic2Diagnosis has been developed for diagnosing cardiovascular diseases (CVD) directly from printed electrocardiogram (ECG) images, bypassing the need for digitization. This approach employs a two-step curriculum learning framework, achieving significant accuracy with an AUC of 0.9534 and an F1 score of 0.7801 on the BHF ECG Challenge dataset.

A novel three-stage training paradigm has been proposed to enhance the interpretability and trustworthiness of deep learning models in classifying electrocardiograms (ECGs) by transferring knowledge from multimodal clinical data into an ECG encoder. This approach utilizes a self-supervised, joint-embedding pre-training stage to enrich ECG representations with contextual clinical information, while only requiring the ECG signal during inference.