Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale...

Area of Science:

• Physiology
• Biomedical Engineering
• Time Series Analysis

Background:

• Multiscale entropy (MSE) and refined multiscale entropy (RMSE) are used to analyze time series complexity.
• Existing methods may inaccurately assess heart rate variability (HRV) complexity in certain pathologies.
• Limitations include fixed thresholding and insensitivity to short-term variability.

Purpose of the Study:

• To propose an improved refined multiscale entropy (I-RMSE) technique for more accurate HRV complexity assessment.
• To address limitations of existing MSE and RMSE methods in evaluating signal complexity.
• To validate the I-RMSE technique on simulated and real HRV data.

Main Methods:

• Developed an improved RMSE (I-RMSE) by introducing a novel thresholding procedure.
• The new threshold considers period-to-period variability within the signal.
• Evaluated I-RMSE on simulated datasets and real-world HRV databases.

Main Results:

• I-RMSE consistently assigned higher entropy to healthy subjects compared to patients with atrial fibrillation, congestive heart failure, sudden cardiac death, and diabetes mellitus.
• The technique demonstrated reduced HRV complexity in female, elderly, and severely ill patient groups.
• Findings were consistent across all time scales and for surrogate time series.

Conclusions:

• The proposed I-RMSE technique offers a more accurate assessment of HRV complexity.
• Reduced HRV complexity is linked to aging, female sex, and severe cardiovascular/non-cardiovascular diseases.
• I-RMSE provides a reliable tool for analyzing HRV in clinical and research settings.