An Insight to AI-Powered Cardiovascular Health Monitoring System using Vocal Biomarkers

Cardiovascular Diseases (CVDs) remain high on the list of causes of death globally, so early, convenient, and non-invasive diagnostic possibilities are required. Conventional cardiovascular monitoring relies on clinical- grade equipment and physical checks, which limit scalability and real-time measurement. The current paper presents a design for an AI-driven framework for cardiovascular health monitoring via speech biomarkers measurable voice parameters representing physiological change due to cardiac disease. The proposed methodology integrates acoustic preprocessing; feature extraction techniques such as Mel- Frequency Cepstral Coefficients (MFCCs), jitter, and breath-to-speech ratio; and classification using machine learning and deep learning architectures such as support vector machines, convolutional neural networks, and recurrent neural architectures. Since proper public datasets with annotated cardiovascular speech data are lacking, speech recordings will be collected manually via pilot trials in healthy volunteers and subsequently via clinical collaborations for extensive validation. The project is currently in the design phase, and the paper introduces the research aims, system structure, data collection plan, and implementation plan for the development of a non-invasive, scalable, real-time cardiovascular monitoring system.