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[Hilbert transform-based modelling of EKG]
Meditsinskaia Tekhnika
|August 19, 2005
Summary
This study introduces an adaptive parametric modeling technique for analyzing non-linear, non-stationary electrocardiogram (ECG) data. The method accurately estimates instantaneous phase and module directly from ECG beats for improved time-frequency analysis.
Area of Science:
- Signal Processing
- Biomedical Engineering
- Time-Frequency Analysis
Context:
- Analyzing non-linear and non-stationary biomedical signals like electrocardiograms (ECG) presents significant challenges.
- Traditional methods may struggle with the complex dynamics inherent in ECG data.
Purpose:
- To develop and evaluate an adaptive parametric modeling technique for ECG analysis.
- To estimate the instantaneous module and phase directly from individual ECG beats.
- To improve the time-frequency analysis of non-linear and non-stationary biomedical signals.
Summary:
- This paper presents a novel adaptive parametric modeling approach for ECG signal analysis.
- The technique models the instantaneous module and phase, with parameters estimated adaptively from a reference signal for each ECG beat.
- Performance is validated using data from the MIT-BIH arrhythmia database.
Impact:
- Offers a more accurate method for characterizing complex ECG dynamics.
- Enhances the capability of time-frequency analysis for biomedical signals.
- Provides a foundation for improved automated detection and diagnosis of cardiac arrhythmias.