Work

Novel Machine Learning Fusion Architectures Integrating Electrocardiogram Representations: Applications to Acute Coronary Event Detection

Year: 2026

Type: article

Abstract: Background and Aims: AI in electrocardiography (ECG) has diverged into two paths: traditional signal processing with machine learning, and deep learning of raw waveforms. The former preserves physiological interpretability but may miss novel patterns, while the latter overlooks a century of ECG science. No framework yet integrates both. This study introduces a fusion model combining handcrafted feature-based random forest and waveform-based convolutional neural network to improve diagnosis of acute coronary events. Methods: This prospective, multicentre cohort study included prehospital ECGs recorded on emergency medical services from patients with a chief complaint of chest. Using this dataset, we developed four fusion frameworks that integrate ECG features with waveform information: (1) decision-level fusion combining predictions from random forest and CNN; (2) decision-level fusion with retraining; (3) feature-level fusion by concatenating CNN embeddings with ECG features; and (4) feature-level fusion combining CNN embeddings with tree-level outputs from the random forest. Results: This study comprised of 10393 ECGs from 7397 patients. On both outcomes, ACS and OMI, the proposed decision fusion with retraining (Approach 2) achieved an AUROC of 0.878 (95% CI, 0.837–0.915) and 0.961 (95% CI, 0.934–0.983) on the test set, and an average precision score of 0.749 (95% CI, 0.681–0.810) and 0.850 (95% CI, 0.763–0.918), respectively, outperforming all other proposed fusion and individual models. Conclusions: This study demonstrates the significance of fusion models that integrate complementary ECG representations to improve diagnostic accuracy in acute coronary events, highlighting a promising approach for real-time AI-augmented cardiovascular disease detection across diverse use cases.

Source: Carolina Digital Repository (University of North Carolina at Chapel Hill)

DOI: 10.17615/cqab-hn20