Criteria for the Deployment of Machine Learning Models for ECG Analysis for Edge Computing

Portable electrocardiogram (ECG) systems are becoming increasingly popular, which is also driving demand for efficient real-time analyses that comply with data protection regulations. The field of edge computing offers novel approaches to signal processing at the point of data acquisition, reducing latency and enabling a higher level of data security. In this context, the FACE project aims to leverage the strengths of both edge computing and cloud computing to develop an innovative machine learning (ML) solution for analyzing long-term electrocardiograms. This contribution analyses various criteria for the application of machine learning models for ECG analysis in edge computing in terms of their applicability in the project. In addition, the determination of the necessary hardware will be based on the resulting criteria derived from the literature review.