Key result
The db6 wavelet function detected the maximum number of correct prominent R-peaks (11 correct, 2 false) and yielded a mean of cycles (8.578) closest to the original signal.
Why the study?
Clinical interpretation of ECG is hindered by noise and artifacts from poor recording environments, necessitating wavelet-based denoising methods to accurately detect R peaks.
Answers are grounded in this paper and cite their sources.
The db6 wavelet function is highly effective for denoising ECG signals and accurately detecting R-peaks compared to other wavelet functions.
The diseases which are associated with the human heart can be detected through a procedure known as the electrocardiogram that is an electrical activity of the human heart. The clinical analysis of ECG faces problems in its interpretation due to the poor recording environment where unwanted noise is getting contaminated into it in the form of different artifacts viz motion artifacts, loose lead artifact, baseline wandering, muscle tumour artifacts etc. To get noise-free electrocardiogram wavelet theory plays an important role. This paper talks about the framework of approximation of an electrocardiogram signal by wavelet theory. On obtaining the denoised electrocardiogram, prominent peaks and R peaks -part of QRS complex are detected. The proposed algorithm is applied to the ECG recording No. 100m.mat obtained from MIT-BIH database.
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Wani et al. (2022) studied Electrocardiogram signal analysis. Wavelet Transform and Wavelet Approximation algorithm vs. Various wavelet functions (Haar, Daubechies, Coiflet, Symlet) was evaluated on Detection of prominent R-peaks and mean of cycles. The db6 wavelet function detected the maximum number of correct prominent R-peaks (11 correct, 2 false) and yielded a mean of cycles (8.578) closest to the original signal.
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