An Intelligent Safety‐Driven Design and Verification of ECG Telehealth Systems: A Model‐Based Approach and Case Study

ABSTRACT The increasing adoption of telehealth systems reflects a broader shift towards personalized, non‐hospital‐based care for patients with chronic conditions. However, ensuring the dependability of these systems, encompassing safety, reliability, and security, is essential as system failures can result in delayed or omitted treatments with potentially life‐threatening consequences. This paper presents an integrated methodology for Model‐based Dependability Analysis (MBDA) applied to the design of an Electrocardiogram (ECG) telehealth system. The approach combines automated Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA) with formal verification via model checking. The analysis facilitates the early identification of critical failure modes, including those with Machine Learning (ML) components, and guides the refinement of system architecture and fault‐tolerant mechanisms. We demonstrate how the integration of these techniques supports an iterative, safety‐driven development process and enhances the robustness of the final system design.