A Novel Framework for Real-Time ECG and Blood Pressure Signal Analysis: Enhancing Accuracy and Interaction through Dynamic Quality Evaluation

Real-time electrocardiogram (ECG) and blood pressure signal analysis is crucial for cardiovascular health monitoring, particularly in critical care and unsupervised settings. However, challenges such as noise interference, limited user interaction, and difficulties in computing accurate physiological metrics persist. This study proposes a novel framework that integrates real-time signal detection with automatic signal quality evaluation, enabling researchers to manage noise through an interactive interface. By leveraging open-source software and introducing a dynamic statistical method for signal quality evaluation, the framework provides a more adaptable and dataset-independent solution. The study aims to enhance interaction between real-time signals and researchers, evaluate signal quality in real time, and analyze baroreceptor sensitivity. Ultimately, this framework improves the accuracy and reliability of cardiovascular signal analysis, leading to better clinical and research outcomes.