Intelligent Electrocardiogram Analysis Technology System: From Deep Representation Learning to Clinical Multi-Scenario Applications

This study aims to establish a comprehensive intelligent ECG analysis technology system encompassing three core dimensions: technical methodologies, performance evaluation, and clinical applications. At the technical methodology level, the research integrates hybrid denoising strategies combining traditional filtering with deep learning, leveraging mainstream network architectures such as ResNet-BiLSTM and Transformer to achieve a paradigm shift from manual feature extraction to end-to-end representation learning. To address data scarcity, generative techniques like the Diffusion Denoising Probability Model (DDPM) were introduced. The study also explored cross-lead spatial feature fusion and multimodal learning pathways to effectively capture the correlation between cardiac mechanical and electrical activities. Regarding the evaluation framework, a multidimensional metric matrix encompassing algorithm recognition capability, signal generation fidelity, and clinical efficacy was constructed. The scientific rigor and reliability of the technology were ensured through metrics including F1 score, percentage root mean square error, and physician agreement. Regarding application deployment, the study emphasizes the use of millimeter-wave radar-based non-contact monitoring systems in emergency and intensive care settings, alongside the potential of wearable devices for distributed health monitoring. Finally, it analyzes regulatory certification requirements for clinical translation, providing a comprehensive reference framework for building an inclusive, trustworthy intelligent cardiovascular health monitoring system.