Key result
Automatic detection using DWT and hierarchical clustering achieves ~100% sensitivity for R-peaks.
Why the study?
Accurate detection and delineation of QRS-complexes and T-waves in ECG signals are crucial for identifying cardiac abnormalities including ventricular arrhythmias leading to sudden cardiac death.
The proposed algorithm using hierarchical clustering and DWT provides highly accurate automatic detection of R and T peaks in ECG signals.
High benchmark accuracy extends algorithmic options for ECG analysis; leaves open prospective clinical validation.
The detection and delineation of QRS-complexes and T-waves in Electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and Discrete Wavelet Transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the Modulus-Maxima Analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.
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Chen et al. (2020) studied Cardiac abnormalities and ventricular arrhythmias. Automatic R and T peak detection method based on hierarchical clustering and Discrete Wavelet Transform vs. Manual and automatic database annotations was evaluated on R-peak and T-peak detection sensitivity, positive predictivity, and accuracy. An automatic R and T peak detection method using hierarchical clustering and DWT achieved 99.89% sensitivity and 99.97% positive predictivity for R-peaks on the MIT-BIH database.
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