Abstract
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.
Highlights
The electrocardiogram (ECG) is a quasi-periodic biomedical signal that provides information about cardiac muscle electrical activities
We propose a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model, to locate R-peaks in the ECG signal
The accurate delineation of R-peaks in the ECG signal plays a crucial role in most automated ECG analysis tools
Summary
The electrocardiogram (ECG) is a quasi-periodic biomedical signal that provides information about cardiac muscle electrical activities. We exploit the model introduced by Hocking et al [26, 27], in which a graph-based optimal changepoint detection model was used for detecting abrupt changes in the genomics data In their work, they propose a new class of functional pruning algorithms with log-linear time complexity in the amount of data, which is capable of handling the large datasets that are common to ECG analysis. To the best of our knowledge, this is the first study in which changepoint detection models have been proposed to detect ECG fiducial points in long records of ECG signals In this novel framework, prior biological knowledge about the expected sequence of changes can be specified in a constraint graph.
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