Pearson correlation–based Dynamic Time Warping reduced mean QTcB error to 28.14 ms vs. 124.54 ms with conventional DTW and had a mean ECG delineation error of 10.68 ms.
A novel Pearson correlation-based Dynamic Time Warping method significantly improves the accuracy of ECG waveform boundary detection without the need for large annotated training datasets.
Absolute Event Rate: 0% vs 0%
Electrocardiogram waveform delineation is a fundamental task for quantitative cardiac analysis, yet accurate and consistent estimation of waveform boundaries remains challenging due to heart rate variability, inter-subject morphological differences, and nonlinear temporal distortions across cardiac cycles. Conventional rule-based methods and pointwise Dynamic Time Warping approaches are sensitive to amplitude variations and baseline fluctuations, while deep learning–based models require large annotated datasets and often suffer from limited interpretability and generalization. In this study, we propose a morphology-oriented ECG waveform alignment framework based on Pearson correlation–based Dynamic Time Warping (PCDTW). By integrating window-level matching with a correlation-driven cost function, the proposed method explicitly emphasizes local morphological similarity rather than absolute amplitude differences. Each ECG record is aligned using a subject-specific reference cycle constructed from normalized RR intervals, enabling stable correspondence of waveform boundaries without any training process. The proposed method was evaluated on two publicly available databases, the QT Database (QTDB) and the Lobachevsky University Electrocardiography Database (LUDB). Experimental results show that PCDTW significantly reduces QT and QTcB estimation errors compared with conventional DTW variants, demonstrating improved temporal consistency and lower bias across cardiac cycles. In particular, the mean QTcB error was reduced to 28.14 ms, compared with 124.54 ms obtained using conventional DTW. In addition, on LUDB, the overall mean delineation error for the P wave, QRS complex, and T wave boundaries was 10.68 ms, showing comparable or superior performance to state-of-the-art deep learning–based methods despite requiring no external training data. These findings indicate that morphology-aware, correlation-based temporal alignment provides a robust and interpretable alternative for ECG waveform boundary detection under realistic physiological variability.
Lee et al. (Sat,) reported a other. Pearson correlation–based Dynamic Time Warping reduced mean QTcB error to 28.14 ms vs. 124.54 ms with conventional DTW and had a mean ECG delineation error of 10.68 ms.