부산대학교 도서관

In order to analyze Electrocardiograms (ECGs) in real time, this research is the first to integrate Recurrent Neural Networks (RNNs) into a cloud-based healthcare system. By utilizing sophisticated computational algorithms and an interpretivist perspective, the research seeks to transform cardiac diagnosis. Though extremely useful, traditional ECG analysis techniques have processing speed as well as scalability issues. The suggested method instantaneously interprets ECG waveforms by utilizing RNNs' temporal modeling capabilities. This paradigm change makes it possible to identify cardiac problems in a timely manner, potentially improving patient outcomes. Setting up a scalable cloud system for effective data processing is part of the study methodology. The RNN model is trained, verified, and then incorporated into the cloud system using secondary ECG data. The system's efficacy is confirmed by performance evaluation criteria like processing speed, sensitivity, and accuracy. Scalability, safety of data, and interpretability issues in models are highlighted via critical analysis. Techniques for improved model openness, strict data protection policies, and thorough scalability testing are all included in the recommendations.