[Study of the variations in cardiovascular system indicators using the main component method]

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Data Analysis

Background:

• Assessing cardiovascular system stress in healthy individuals at rest is crucial for preventative health.
• Traditional methods may not fully capture subtle physiological responses to stress.
• The Valsalva maneuver is a well-established tool for evaluating autonomic cardiovascular regulation.

Purpose of the Study:

• To investigate the utility of principal component analysis (PCA) for identifying cardiovascular stress.
• To analyze deviations in heart rate, mean blood pressure, and stroke volume during a Valsalva maneuver.
• To differentiate between true cardiovascular stress and variations due to weak physiological responses.

Main Methods:

• Applied the main component method (Principal Component Analysis) to cardiovascular measurements from 14 healthy operators.
• Analyzed 237 observations, focusing on deviations of heart rate, mean blood pressure, and stroke volume from individual baseline values.
• Baseline values were established from measurements recorded during the Valsalva maneuver.

Main Results:

• The first two principal components explained over 77% of the total variance in cardiovascular parameters.
• Peripheral data points, distant from the main data cluster, suggested potential cardiovascular stress.
• Two-thirds of these 'stressed' points were linked to subjects with a weak Valsalva maneuver response, while one-third indicated true stress.

Conclusions:

• Deviations from individual cardiovascular baselines, analyzed via PCA and considering the Valsalva maneuver, can effectively identify a stressed cardiovascular system.
• This method shows promise for detecting subclinical cardiovascular stress in healthy men at rest.
• Distinguishing true stress from weak physiological responses is possible using this analytical approach.