Research and Design of a CNN-LSTM-Based ECG Atrial Fibrillation Recognition System

Atrial fibrillation exhibits a high incidence rate and poses considerable risks within cardiovascular diseases, necessitating both early detection and continuous monitoring. However, because of the intricate morphology of electrocardiogram (ECG) signals and marked inter-individual variability, current manual diagnostic procedures and standard automated recognition methods still encounter limitations in accuracy and real-time performance. In response, this study employs a CNN to capture localized waveform features and incorporates an LSTM to learn broader temporal dependencies. By introducing Focal Loss to address imbalanced data, a fully automated AF detection system is established. When integrated into an end-to-end system framework, this approach offers prompt and efficient AF alerts suitable for home-based monitoring and telemedicine. Situated at the intersection of deep learning and physiological signal analysis, this research demonstrates potential practical value and provides a feasible strategy for intelligent recognition of AF and other arrhythmias.