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A mathematical algorithm for ECG signal denoising using window analysis.

Hamid SadAbadi¹, Masood Ghasemi, Ali Ghaffari

¹CardioVascular Research Group, Department of Mechanical Engineering, K. N. Toosi University of Technology, No. 15, Pardis St., MollaSarda Ave., Vanak sq., Tehran, Iran, P.O. Box 19395-1999. hamidsadabadi@sina.kntu.ac.ir

Biomedical Papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia

|August 11, 2007

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This study introduces a simple, fast ECG signal processing method using a moving window to remove noise and baseline wander. The technique effectively preserves ECG wave characteristics, offering a viable solution for artifact suppression.

Area of Science:

Biomedical Engineering
Signal Processing

Background:

Parasitic interference signals, such as electromyogram (EMG) artifacts, pose significant challenges in electrocardiogram (ECG) signal registration.
Existing methods for ECG artifact suppression include wavelet shrinkage, time-frequency dependent thresholds, artificial neural networks, and energy thresholding.

Purpose of the Study:

To develop and evaluate a novel windowed technique for suppressing noise and baseline wander in ECG signals.
To assess the effectiveness of the proposed method in preserving essential ECG wave characteristics.

Main Methods:

A mathematical method was developed utilizing dominant QRS complex scales and their domains.
A varying-length window was employed to process the entire ECG signal.
The method was evaluated for both artificially corrupted and actual recorded ECG signals.

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Main Results:

The proposed method successfully removed high-frequency noise and low-frequency baseline wander from ECG signals.
The technique demonstrated effectiveness in preserving the intrinsic characteristics of ECG waves.

Conclusions:

The developed algorithm is simple, fast, and preserves ECG wave characteristics, making it a suitable option for artifact suppression.
Potential challenges include the dependency on pre-stage QRS complex detection and parameter adaptation for diverse ECG morphologies.