A Functional Data Analysis Approach to Left Ventricular Remodeling Assessment.

Area of Science:

• Cardiology
• Biomedical Engineering
• Data Science

Background:

• Left ventricular remodeling is a key process in cardiovascular diseases, impacting myocardial morphology.
• Current diagnostic parameters like ejection fraction primarily assess volumetric aspects, potentially overlooking structural changes.
• Quantitative assessment of structural modifications is crucial for early disease detection.

Purpose of the Study:

• To propose a functional data analysis approach for evaluating myocardial contractility.
• To develop a method for discriminating between healthy subjects and those with left ventricular hypertrophy.
• To integrate higher informative content beyond traditional clinical parameters for assessing cardiac morphology.

Main Methods:

• Introduction of a functional representation of ventricular shape.
• Application of functional principal component analysis (FPCA).
• Utilization of depth measures for subject discrimination.

Main Results:

• The proposed approach successfully discriminates between healthy individuals and those with left ventricular hypertrophy.
• Functional data analysis provides a synthetic representation of myocardial morphological changes.
• The method integrates more informative content compared to standard clinical parameters.

Conclusions:

• Functional data analysis offers a promising tool for quantitative assessment of myocardial contractility and structural changes.
• This approach can aid in the early individuation of left ventricular hypertrophy.
• The method has potential for implementation in future clinical practice for improved cardiovascular disease management.