[The analysis method of the Hilbert spectrum entropy of dividing frequency range for signals of heart rate variability]

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Context:

• Heart rate variability (HRV) signal analysis is crucial for diagnosing and monitoring heart disease.
• Existing time-frequency entropy methods may not fully capture complex physiological characteristics.
• Accurate differentiation of cardiac conditions is essential for effective patient management.

Purpose:

• To propose a new HRV signal analysis method based on Hilbert spectrum entropy, dividing the frequency range.
• To calculate Hilbert time-frequency spectrum entropy within specific frequency bands and overall, using a weighting factor.
• To enhance the reflection of physiological and pathological characteristics by separating frequency ranges.

Summary:

• A novel method calculates Hilbert spectrum entropy by dividing HRV signals into frequency ranges.
• This approach analyzes multi-resolution Hilbert spectrum characteristics and HRV frequency spectrum properties.
• The method was validated on the MIT-BIH database, differentiating young, elderly, atrial fibrillation, healthy, and CHF patient groups.

Impact:

• The proposed method effectively differentiates between various physiological and pathological groups.
• It demonstrates superior statistical analysis performance compared to general time-frequency entropy methods.
• This approach offers a potentially valuable tool for clinical HRV signal analysis and cardiac health assessment.