An ECG feature extraction system based on multiresolution wavelet transform achieved a QRS detection sensitivity of 75.2% and a positive predictivity of 45.4% on the MIT-BIH database.
Does a multiresolution wavelet transform-based feature extraction system accurately detect QRS complexes and ECG wave peaks in ECG signals?
A multiresolution wavelet transform-based algorithm can extract ECG features including QRS complexes and P/T wave boundaries.
In this work, we have developed and evaluated an electrocardiogram (ECG) feature extraction system based on the multi-resolution wavelet transform. ECG signals from Modified Lead II (MLII) are chosen for processing. The result of applying two wavelet filters (D4 and D6) of different length on the signal is compared. The wavelet filter with scaling function more closely to the shape of the ECG signal achieved better detection. In the first step, the ECG signal was de-noised by removing the corresponding wavelet coefficients at higher scales. Then, QRS complexes are detected and each complex is used to locate the peaks of the individual waves, including onsets and offsets of the P and T waves which are present in one cardiac cycle. We evaluated the algorithm on MIT-BIH Database, the manually annotated database, for validation purposes. The proposed QRS detector achieved sensitivity of 75. 2 % 18 . 99 .. and a positive predictivity of 45 . 4 % 00 . 98 .. over the validation database.
Mahmoodabadi et al. (Sat,) conducted a other in ECG signal processing. Multiresolution wavelet transform vs. D4 vs D6 wavelet filters was evaluated on QRS detection sensitivity and positive predictivity. An ECG feature extraction system based on multiresolution wavelet transform achieved a QRS detection sensitivity of 75.2% and a positive predictivity of 45.4% on the MIT-BIH database.