A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection
This work provides an incremental improvement in automatic QRS complex detection for cardiovascular disease diagnosis, benefiting clinicians and patients by improving the accuracy of ECG analysis.
This study proposes a new method for automatic QRS complex detection in ECG signals using a graph-constrained changepoint detection (GCCD) model. The model achieved a sensitivity of 99.64% and positive predictivity of 99.71% with manually defined graphs, and 99.76% sensitivity and 99.68% positive predictivity with automatically learned graphs on the MIT-BIH Arrhythmia Database.
The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal; thereby, the R-peak detection task can be relaxed from any preprocessing step. In this novel approach, prior biological knowledge about the expected sequence of changes is incorporated into the model using the constraint graph, which can be defined manually or automatically. First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme. Finally, we compare the manually defined graphs and learned graphs in terms of graph structure and detection accuracy. We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.