A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
This work provides an incremental improvement in R-peak detection for medical diagnostics by offering a competitive new method.
This study addresses the problem of R-peak detection in ECG signals by employing a graph-based changepoint detection model. The method achieved a sensitivity of 99.64%, a positive predictivity of 99.71%, and a detection error rate of 0.19% on the MIT-BIH Arrhythmia Database.
This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to other state-of-the-art approaches. The proposed method achieves the overall sensitivity of Sen = 99.64%, positive predictivity of PPR = 99.71%, and detection error rate of DER = 0.19.