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A dynamic Fourier series for the compression of ECG using FFT and adaptive coefficient estimation
1Electronic Engineering Department, Hijjawi Faculty for Applied Engineering, Yarmouk University, Irbid, Jordan.
Medical Engineering & Physics
|April 1, 1995
Summary
This study introduces a novel electrocardiogram (ECG) data compression method using dynamic Fourier series modeling. The technique efficiently compresses ECG signals, showing promise for improved data management in healthcare.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Medical Informatics
Background:
- Electrocardiogram (ECG) data is crucial for diagnosing cardiac conditions.
- Efficient ECG data compression is needed to reduce storage and transmission burdens.
- Existing compression methods may not fully capture the dynamic nature of ECG signals.
Purpose of the Study:
- To propose a new ECG data compression technique.
- To model quasi-periodic ECG signals using a dynamic Fourier series.
- To evaluate the performance of the proposed compression method.
Main Methods:
- Modeling quasi-periodic ECG signals as a dynamic Fourier series.
- Continuously estimating Fourier coefficients using Fast Fourier Transform (FFT) algorithm.
- Continuously estimating Fourier coefficients using adaptive least mean square (LMS) algorithm.
Main Results:
- Simulated results for normal and pathological ECGs were presented.
- The merits of both FFT and adaptive LMS algorithms for coefficient estimation were illustrated.
- The proposed dynamic Fourier series method was compared with other compression techniques.
Conclusions:
- The dynamic Fourier series approach offers a viable method for ECG data compression.
- Both FFT and adaptive LMS algorithms demonstrate effectiveness in estimating Fourier coefficients.
- The proposed technique shows potential for efficient ECG data management.