Architecture of a Cloud Platform for Remote Cardiac Monitoring: A Practical Guide to Scaling and Processing ECG Telemetry

Oleksandr Kydiuk

Citation: Oleksandr Kydiuk, "Architecture of a Cloud Platform for Remote Cardiac Monitoring: A Practical Guide to Scaling and Processing ECG Telemetry", Universal Library of Medical and Health Sciences, Volume 04, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The methodology examines the architecture of a cloud platform for remote cardiac monitoring intended for continuous acquisition, transmission, analysis, and clinical interpretation of ECG telemetry at the scale of a large distributed system. The study’s relevance lies in the growing demand for early arrhythmia detection and the transition of cardiology toward a model of prolonged observation outside the hospital. The purpose of the methodology is to formulate a practical guide to the design and scaling of a platform that integrates wearable sensors, secure communication channels, an event-driven backend, storage layers, alerting algorithms, and mechanisms for integration with medical information systems. The scientific novelty of the study lies in consolidating, within a single engineering framework, the requirements for fault tolerance, data security, ECG time-series processing, automatic report generation, and interoperability via HL7 FHIR. It is shown that a multilayer event-driven architecture provides resilient telemetry intake, maintains clinical integrity of the time series, reduces latency in detecting hazardous episodes, and ensures compliance with HIPAA and GDPR requirements. The methodology will be useful for HealthTech engineers, system architects, developers of medical platforms, and specialists in digital healthcare.


Keywords: Remote Cardiac Monitoring, ECG Telemetry, Cloud Architecture, Microservices, Event-Driven Processing.

Download doi https://doi.org/10.70315/uloap.ulmhs.2026.0403007