[The study of the pulse signals of atherosclerosis based on Hilbert-Huang transform and sample entropy]

Area of Science:

• Biomedical Engineering
• Cardiovascular Research
• Signal Processing

Context:

• Atherosclerosis is a significant cardiovascular disease with severe health implications.
• Early diagnosis of atherosclerosis is crucial for timely intervention and improved patient outcomes.
• Traditional diagnostic methods may have limitations in detecting early-stage atherosclerosis.

Purpose:

• To investigate the potential of using pulse signal analysis for the early diagnosis of atherosclerosis.
• To differentiate between pulse signals of healthy individuals and patients with atherosclerosis.
• To evaluate the efficacy of Hilbert-Huang Transform (HHT) and sample entropy in this diagnostic context.

Summary:

• Pulse signals were collected from healthy adults and patients with atherosclerosis.
• Empirical Mode Decomposition (EMD) processed the pulse signals, followed by sample entropy calculation for each intrinsic mode function (IMF).
• Analysis revealed significantly lower sample entropy in the first IMF of atherosclerosis patients compared to healthy controls.
• Hilbert-Huang Transform (HHT) analysis showed a notable shift of energy towards lower frequencies (0-1 Hz) in patients, with significantly higher energy values in this range compared to the healthy group.
• Statistical analysis, including t-tests, confirmed significant differences between the two groups.

Impact:

• The findings suggest that HHT and sample entropy analysis of pulse signals can effectively distinguish between healthy individuals and those with early-stage atherosclerosis.
• This non-invasive approach holds promise for developing new, accessible tools for early atherosclerosis detection.
• Successful early diagnosis can lead to prompt treatment, potentially mitigating disease progression and reducing cardiovascular events.