Key result
A new algorithm for ECG waves segmentation using wavelet transform and a surface indicator achieved a sensitivity of 99.35% and a positive predictivity of 99.05% on the MIT BIH database.
Why the study?
Does a new algorithm based on wavelet transform and surface indicator accurately segment ECG waves in signals from the MIT BIH database?
Does a new algorithm based on wavelet transform and surface indicator accurately segment ECG waves in signals from the MIT BIH database?
A novel algorithm utilizing wavelet transform and a surface indicator demonstrates high sensitivity and positive predictivity for automated ECG wave segmentation.
May aid automated ECG segmentation research; leaves open prospective clinical validation.
In this paper, a new algorithm for ECG waves segmentation is described. The algorithm is based on the wavelet transform for the complex QRS delineation and a surface indicator for the detection of the T-end waves. The described algorithm was evaluated using ECG signals from the universal database MIT BIH. A sensitivity of 99.35% and a positive predictivity of 99.05% are reached. Those obtained results show the good performances of this new algorithm.
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Dib et al. (2011) studied ECG signals. New algorithm for ECG waves segmentation (wavelet transform and surface indicator) was evaluated on ECG waves segmentation (QRS delineation and T-end detection). A new algorithm for ECG waves segmentation using wavelet transform and a surface indicator achieved a sensitivity of 99.35% and a positive predictivity of 99.05% on the MIT BIH database.
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