Topological Data Analysis in Cardiovascular Signals: An Overview.

Area of Science:

• Cardiovascular Research
• Data Science
• Applied Mathematics

Background:

• Cardiovascular diseases (CVDs) represent a significant global health burden.
• Analyzing complex cardiovascular signals is crucial for understanding and managing CVDs.
• Traditional methods may struggle to capture intricate patterns in cardiovascular data.

Purpose of the Study:

• To provide a comprehensive overview of Topological Data Analysis (TDA) applications in cardiovascular signal analysis.
• To explore TDA's potential in enhancing the understanding and treatment of cardiovascular diseases.
• To review current literature on TDA for cardiovascular signals.

Main Methods:

• Introduction to core TDA concepts: persistent homology, Mapper, and multidimensional scaling.
• Review of TDA applications across various cardiovascular signals (ECG, PPG, arterial stiffness).
• Discussion of TDA's role in diagnosis, prognosis, and clinical practice.

Main Results:

• TDA effectively disentangles complex data structures in cardiovascular signals.
• TDA techniques like persistent homology and Mapper reveal non-trivial patterns.
• Applications span electrocardiography, photoplethysmography, and arterial stiffness analysis.

Conclusions:

• Topological Data Analysis shows significant promise for cardiovascular signal analysis.
• TDA can offer deeper insights into cardiovascular diseases and their management.
• Future directions include broader clinical integration and methodological advancements.