Key result
A sparse representation-based algorithm accurately approximated original ECG signals, removed noise, and detected QRS complexes, demonstrating robustness compared to state-of-the-art methods.
A novel sparse representation-based algorithm effectively enhances ECG signals and detects QRS complexes by modeling signals as superpositions of learned inner structures.
Electrocardiogram (ECG) signal enhancement and QRS complex detection is a critical preprocessing step for further heart disease analysis and diagnosis. In this paper, we propose a sparse representation-based ECG signal enhancement and QRS complex detection algorithm. Unlike traditional Fourier or wavelet transform-based methods, which use fixed bases, the proposed algorithm models the ECG signal as the superposition of a few inner structures plus additive random noise, where these structures (referred to here as atoms) can be learned from the input signal or a training set. Using these atoms and their properties, we can accurately approximate the original ECG signal and remove the noise and other artifacts such as baseline wandering. Additionally, some of the atoms with larger kurtosis values can be modified and used as an indication function to detect and locate the QRS complexes in the enhanced ECG signals. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-of-the-art ECG enhancement and QRS detection algorithms using both simulated and real-life ECG recordings.
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Zhou et al. (2016) studied ECG signal enhancement and QRS complex detection. Sparse representation-based ECG signal enhancement and QRS complex detection algorithm vs. Traditional Fourier or wavelet transform-based methods and other state-of-the-art algorithms was evaluated on ECG signal enhancement and QRS detection accuracy and robustness. A sparse representation-based algorithm accurately approximated original ECG signals, removed noise, and detected QRS complexes, demonstrating robustness compared to state-of-the-art methods.
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