Systolic blood pressure estimation method using electrocardiogram RRI data.

Area of Science:

• Physiology
• Complex Systems Analysis
• Biomedical Engineering

Background:

• Arterial pressure regulation involves complex mechanisms.
• Existing models like the Guyton model provide a framework for understanding these systems.
• Non-nervous intermediate pressure control mechanisms play a crucial role.

Purpose of the Study:

• To propose a novel chaos time series analysis method for non-nervous intermediate pressure control.
• To investigate the relationship between electrocardiogram RRI data and arterial pressure.
• To identify parameters that grasp chaotic variations in pressure control.

Main Methods:

• Utilized an electrocardiogram RRI delay coordinate system to obtain responses of intermediate pressure control mechanisms.
• Determined two frequency ranges using residual functions and best approximation functions to identify action and compensation.
• Estimated control object values using gradients of tangent and quantities of state at inflection points.

Main Results:

• Developed a polynomial creating a 3D response surface (R²=0.814) correlating EKG RRI gradients with brachial systolic blood pressure in 225 cases.
• Validated the model with EKG RRI data from 120 readings of one subject, showing strong correlation (R²=0.8564) with measured brachial systolic blood pressure.
• Demonstrated that gradients of tangent are effective parameters for capturing chaotic variations in intermediate pressure control mechanisms.

Conclusions:

• The proposed chaos time series analysis method is effective for studying non-nervous intermediate pressure control.
• Gradients derived from EKG RRI data serve as valuable parameters for understanding arterial pressure regulation dynamics.
• This approach offers a novel perspective on analyzing physiological control systems using chaos theory.