The multiscale morphological derivative (MMD) detector reliably identified ECG characteristic waves, achieving 100% sensitivity for QRS onset and offset with standard deviations within CSE committee limits, generally outperforming threshold- and wavelet-based methods.
The MMD transform-based detector offers a promising automated method for accurate ECG signal analysis and arrhythmia recognition.
BACKGROUND: Detection of characteristic waves, such as QRS complex, P wave and T wave, is one of the essential tasks in the cardiovascular arrhythmia recognition from Electrocardiogram (ECG). METHODS: A multiscale morphological derivative (MMD) transform-based singularity detector, is developed for the detection of fiducial points in ECG signal, where these points are related to the characteristic waves such as the QRS complex, P wave and T wave. The MMD detector is constructed by substituting the conventional derivative with a multiscale morphological derivative. RESULTS: We demonstrated through experiments that the Q wave, R peak, S wave, the onsets and offsets of the P wave and T wave could be reliably detected in the multiscale space by the MMD detector. Compared with the results obtained via with wavelet transform-based and adaptive thresholding-based techniques, an overall better performance by the MMD method was observed. CONCLUSION: The developed MMD method exhibits good potentials for automated ECG signal analysis and cardiovascular arrhythmia recognition.
Sun et al. (2005) studied Electrocardiogram (ECG) signal analysis. Multiscale morphological derivative (MMD) transform-based detector vs. Threshold-based detector (TD) and Wavelet-based detector (WD) was evaluated on Detection sensitivity, mean error, and standard deviation of ECG characteristic wave boundaries (QRS complex, P wave, T wave). The multiscale morphological derivative (MMD) detector reliably identified ECG characteristic waves, achieving 100% sensitivity for QRS onset and offset with standard deviations within CSE committee limits, generally outperforming threshold- and wavelet-based methods.