Key result
The WHAT algorithm successfully detected R peaks in ECG signals at noise levels down to -5 dB, outperforming HAT, WAT, and HWAT methods.
Why the study?
Does the WHAT algorithm improve R peak detection in ECG signals compared to other techniques?
Does the WHAT algorithm improve R peak detection in ECG signals compared to other techniques?
Answers are grounded in this paper and cite their sources.
The proposed WHAT algorithm, combining wavelet and Hilbert transforms with adaptive thresholding, improves R peak detection in ECG signals.
Electrocardiogram (ECG) is one of the most common biological signals which play a significant role in the diagnosis of heart diseases. One of the most important parts of ECG signal processing is interpretation of QRS complex and obtaining its characteristics. R wave is one of the most important sections of this complex, which has an essential role in diagnosis of heart rhythm irregularities and also in determining heart rate variability (HRV). This paper employs Hilbert and wavelet transforms as well as adaptive thresholding method to investigate an optimal combination of these signal processing techniques for the detection of R peak. In the experimental sections of this paper, the proposed algorithms are evaluated using both ECG signals from MIT-BIH database and synthetic data simulated in MATLAB environment with different arrhythmias, artifacts, and noise levels. Finally, by using wavelet and Hilbert transforms as well as by employing adaptive thresholding technique, an optimal combinational method for R peak detection namely WHAT is obtained that outperforms other techniques quantitatively and qualitatively.
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Rabbani et al. (2011) studied ECG signal processing (R peak detection). WHAT algorithm (Wavelet Transform, Hilbert Transform, Adaptive Thresholding) vs. HAT, WAT, and HWAT algorithms was evaluated on R peak detection at various noise levels (SNR). The WHAT algorithm successfully detected R peaks in ECG signals at noise levels down to -5 dB, outperforming HAT, WAT, and HWAT methods.
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