A novel QRS segmentation algorithm using wavelet bases and adaptive thresholding demonstrated high sensitivity and positive predictivity compared to manual annotations.
May support automated QRS detection in ECG monitoring; leaves open prospective clinical validation before adoption.
In this paper, we develop and evaluate a new approach to QRS segmentation based on the combination of two techniques: wavelet bases and adaptive threshold. Firstly, QRS complexes are identified without a preprocessing stage. Then, each QRS is segmented by identifying the complex onset and offset. We evaluated the algorithm on two manually annotated databases, the QT-database and the MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity of 99.02% and a positive predictivity of 99.35% over the first lead of the validation databases (more than 192,000 beats), while for the QT-database, values larger than 99.6% were attained. As for the delineation of the QRS complex, the mean and the standard deviation of the differences between the automatic and the manual annotations were computed. Using QT-database which contains recordings of annotated ECG with a sampling rate of 250 Hz, we obtain the average of the differences not exceeding two sampling intervals, while the standard deviations were within acceptable range of values.
No takes yet. Share an insight, caveat, or question.
Madeiro et al. (2006) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: