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A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With
Yue-Der Lin1,2, Yong-Kok Tan2, Tienhsiung Ku3,4
1Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan.
Sensors (Basel, Switzerland)
|April 28, 2023
Summary
This study introduces the Hilbert-Gauss transform (HGT) for accurate vital sign monitoring. HGT effectively estimates respiratory rate from photoplethysmography and seismocardiogram signals, overcoming limitations of traditional methods.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Physiological Monitoring
Background:
- Frequency estimation is crucial for vital sign monitoring, with Fourier transform and eigen-analysis being common methods.
- Physiological signals are nonstationary and time-varying, necessitating time-frequency analysis (TFA).
- Hilbert-Huang transform (HHT) is a potential TFA tool, but Empirical Mode Decomposition (EMD) and Ensemble EMD (EEMD) suffer from mode mixing and boundary effects.
Purpose of the Study:
- To propose the Hilbert-Gauss transform (HGT) as an alternative to HHT for TFA and frequency estimation.
- To address the limitations of EMD/EEMD, such as mode mixing and boundary effects.
- To evaluate HGT's effectiveness in estimating respiratory rate (RR) from various physiological signals.
Main Methods:
- Gaussian Average Filtering Decomposition (GAFD) was employed as an alternative to EMD/EEMD.
- The GAFD technique was combined with the Hilbert transform to create the Hilbert-Gauss transform (HGT).
- HGT was applied to estimate respiratory rate (RR) from finger photoplethysmography (PPG), wrist PPG, and seismocardiogram (SCG) signals.
Main Results:
- The proposed Hilbert-Gauss transform (HGT) demonstrated effectiveness in estimating respiratory rate (RR).
- Estimated RRs from finger PPG, wrist PPG, and SCG showed excellent reliability.
- Intraclass correlation coefficient (ICC) and Bland-Altman analysis confirmed high agreement with ground truth values.
Conclusions:
- The Hilbert-Gauss transform (HGT) offers a viable solution to overcome the drawbacks of HHT in time-frequency analysis.
- HGT provides a reliable and accurate method for respiratory rate estimation from PPG and SCG signals.
- This novel approach enhances vital sign monitoring capabilities through improved frequency estimation techniques.
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