A novel weighted directed graph algorithm for ECG R-peak detection achieved high accuracies across four datasets, including 99.07% on the MIT-BIH dataset and 99.46% on the QT dataset.
A novel graph-based algorithm for ECG R-peak detection demonstrates high accuracy (>98%) across multiple standard datasets.
Detecting R-peak location is very important in electrocardiogram (ECG) analysis. Most existing algorithms focus on separating extreme data points to find R-peaks without ECG wave morphology consideration. As a result, we propose a new algorithm using graph theory to integrate non-linear connections between states. Specifically, we design a weighted directed graph to represent the structure of an ECG wave where each vertex corresponds to a time index and a state of an ECG signal. Therefore, traversing each edge corresponds to labeling a contiguous segment of the signal as a specific ECG state. Each edge also contains a weight corresponding to a cost function and bias. Dynamic programming is used to determine the shortest path corresponding to the optimal labeling by iterating through the topological ordering of the graph. Three logical flags based on the constraints of the ECG signal’s slope, difference, and shape are also introduced and the slope thresholding value for an R-peak is made adaptive to combat varying morphologies. Four datasets were utilized, with the proposed method showing great potential, achieving an accuracy of 99.07% for the MIT-BIH dataset, 99.46% for the QT dataset, 98.05% for the INCART dataset, and 99.23% for the ST CHANGE dataset.
Suwannoppadol et al. (2026) studied Electrocardiogram (ECG) analysis. Weighted directed graph algorithm for R-peak detection was evaluated on Accuracy of R-peak detection. A novel weighted directed graph algorithm for ECG R-peak detection achieved high accuracies across four datasets, including 99.07% on the MIT-BIH dataset and 99.46% on the QT dataset.