ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Simultaneous monitoring of cardiac and respiratory activity is crucial for comprehensive patient assessment.
• Current methods for ECG-derived respiration (EDR) can be complex and require additional steps.
• There is a need for cost-effective, comfortable, and mobile healthcare solutions.

Purpose of the Study:

• To propose and validate a novel approach for deriving respiration from single-lead ECG signals.
• To enhance the accuracy and simplicity of ECG-derived respiration (EDR) monitoring.
• To compare the proposed method with existing EDR techniques.

Main Methods:

• Decomposition of ECG signals into multicomponent sinusoidal signals using iterated Hilbert transform (IHT).
• Filtering of lower-order amplitude components using Hilbert vibration decomposition (HVD) to extract respiratory information.
• Validation on Fantasia and Apnea-ECG datasets, comparing with PCA, RPA, RSA, QRS slopes, and R-wave angle.

Main Results:

• The proposed EDR technique demonstrated higher median correlation coefficients on both datasets (0.699 and 0.57).
• Achieved the lowest mean absolute error (1.27 and 1.35) and average percentage error (9.3% and 10.2%).
• Outperformed existing methods, especially in elderly subjects, and reduced complexity by eliminating fiducial point detection.

Conclusions:

• The proposed IHT and HVD-based EDR method is effective and accurate for respiration monitoring using single-lead ECG.
• This approach offers advantages in simplicity, reduced complexity, and better agreement in respiratory rates.
• The validated technique provides a promising solution for non-invasive, cost-effective patient monitoring.