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Graphics-processor-unit-based parallelization of optimized baseline wander filtering algorithms for long-term

Thomas Niederhauser, Thomas Wyss-Balmer, Andreas Haeberlin

    IEEE Transactions on Bio-Medical Engineering
    |February 13, 2015
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    Summary
    This summary is machine-generated.

    This study introduces a fast GPU-accelerated wavelet filter to remove baseline wander from long-term ECG recordings. This efficient method significantly reduces computational burden for improved signal quality.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Computational Science

    Background:

    • Long-term electrocardiogram (ECG) recordings are susceptible to noise, particularly baseline wander.
    • Baseline wander is prominent with dry or esophageal electrodes used for prolonged monitoring.
    • Traditional analog filters can cause phase distortion, and offline methods incur significant computational costs.

    Purpose of the Study:

    • To develop and evaluate a graphics processor unit (GPU)-based parallelization method for accelerating offline baseline wander filter algorithms.
    • To compare the efficiency and effectiveness of GPU-accelerated wavelet, finite impulse response (FIR), infinite impulse response (IIR), moving mean, and moving median filters for ECG baseline wander removal.
    • To optimize filter parameters for maximizing signal-to-baseline ratio (SBR) increase.

    Main Methods:

    • Implemented GPU-based parallelization for wavelet, FIR, IIR, moving mean, and moving median filters.
    • Optimized individual filter parameters using ECG data from the Physionet database, superimposed with modeled baseline wander.
    • Conducted Monte-Carlo simulations to assess filter performance across various input SBR levels.

    Main Results:

    • The moving median filter excelled in low input SBR but impacted ECG wave detection.
    • The IIR filter was preferred for high input SBR.
    • The parallelized GPU wavelet filter demonstrated superior speed, being 500x faster than the moving median and 4x faster than the IIR filter.
    • Wavelet filtering of a 7-day high-resolution ECG (64 mega samples) was completed in under 3 seconds.
    • The GPU wavelet filter provided superior baseline wander suppression in low SBR scenarios.

    Conclusions:

    • The GPU-accelerated wavelet filter is the most efficient method for removing baseline wander in long-term ECGs.
    • This approach significantly reduces computational burden associated with prolonged ECG signal processing.
    • The high filtering speed and effectiveness make the GPU wavelet filter ideal for clinical applications requiring real-time or near-real-time analysis.