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ECG denoising using modulus maxima of wavelet transform.

Mohammad Ayat¹, Mohammad B Shamsollahi, Behrooz Mozaffari

¹Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology, Iran.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

Summary

This study introduces an effective electrocardiogram (ECG) denoising method using wavelet transform modulus and adaptive thresholding to reduce noise while preserving signal integrity. The approach enhances signal-to-noise ratio (SNR) for clearer medical signal analysis.

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Area of Science:

Medical Engineering
Signal Processing

Background:

Electrocardiogram (ECG) denoising is crucial in medical engineering.
Improving signal-to-noise ratio (SNR) without distorting the signal is a key challenge.

Purpose of the Study:

To propose a novel method for removing white Gaussian noise from ECG signals.
To enhance ECG signal quality for accurate medical diagnosis.

Main Methods:

Utilizing wavelet transform modulus concepts for singularity analysis and signal reconstruction.
Applying adaptive thresholding to remove noise from the wavelet transform modulus maxima.
Reconstructing the ECG signal after noise reduction.

Main Results:

Successfully removed white Gaussian noise from ECG signals.
Preserved the essential characteristics of the ECG signal.
Improved the signal-to-noise ratio (SNR) of the denoised ECG signals.

Conclusions:

The proposed wavelet transform modulus and adaptive thresholding method is effective for ECG denoising.
This technique offers a viable solution for improving the quality of ECG signals in medical applications.