ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

Alka Gautam (Dongseo University, Korea), Hoon-Jae Lee (Dongseo University, Korea) and Wan-Young Chung (Pukyong National University, Korea)
DOI: 10.4018/978-1-4666-1755-1.ch014
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In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.
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Ecg Signal De-Noising

Aim is to improve ECG signal de-noising method by dividing it in two stages, first stage involves QRS complex detection and second stage is filtering. In the first stage, this paper introduces MSMP and second stage filtering with AAF for ECG signal averaging.

Asynchronous Averaging and Filtering Method (AAF)

Signal averaging is a technique for combining the signals (images) recorded from various sources. Signal averaging is applied in varies applications to improve SNR of periodic signal mixed with random Gaussian noise. In cardiology, its main application is in the detection of ventricular late potential (Freedman, Gillis, Keren, Soderholm-Difatte, & Mason, n.d.).

Generally signal averaging technique is implemented wherein:

  • 1.

    Noisy ECGs are time aligned with the mean or median ECG signal

  • 2.

    Signal and noise are uncorrelated

  • 3.

    The nature of noise is random with a mean of zero is true.

Then averaging distorted signal m times improves SNR by a factor of m½ only when the random noise level of each portion of waveform has same characteristic and variability of waveform pattern is small (Herrera-Bendezu, Denys, & Reddy, n.d.) and (Alperin & Sadeh, 1986).

Signal averaging is based on the some characteristics of the signal and the noise like:

  • 1.

    Signal waveform should be periodic

  • 2.

    Noise must be random (not periodic) and uncorrelated with the signal

  • 3.

    Temporal position of each signal waveform must be accurately known (Valtino, Thompkins, & Nquyen, 1996).

AAF algorithm is basically a modified moving average filtering method. It includes two stages for two types of noise (EMG and baseline wander noise) cancellation. First M-point averaging for EMG noise cancellation and second double mean averaging for baseline wander cancellation.

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