Key result
GPU-based parallelized wavelet filtering processed a seven-day high-resolution ECG in less than 3 seconds, operating 500 and 4 times faster than infinite impulse response and moving median filters.
GPU-based parallelization of wavelet filtering is a highly efficient method to remove baseline wander in long-term ECGs, significantly reducing computational burden.
May enable efficient long-term ECG analysis in practice; leaves open prospective clinical validation.
Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here, we present a graphics processor unit (GPU)-based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to autoregressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and four times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a seven-day high-resolution ECG is computed within less than 3 s. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.
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Niederhäuser et al. (2015) studied Baseline wander in long-term electrocardiography. GPU-based parallelization of wavelet filter vs. Infinite impulse response and moving median filters was evaluated on Signal-to-baseline ratio (SBR) increase and computational speed. GPU-based parallelized wavelet filtering processed a seven-day high-resolution ECG in less than 3 seconds, operating 500 and 4 times faster than infinite impulse response and moving median filters.
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