Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs

More than a century has passed since Einthoven laid the foundation of modern electrocardiography and in recent years, driven by the advance of wearable and low budget devices, a sample accurate detection of R-peaks in noisy ECG-signals has become increasingly important. To accommodate these demands, we propose a new R-peak detection approach that builds upon the visibility graph transformation, which maps a discrete time series to a graph by expressing each sample as a node and assigning edges between intervisible samples. The proposed method takes advantage of the high connectivity of large, isolated values to weight the original signal so that R-peaks are amplified while other signal components and noise are suppressed. A simple thresholding procedure, such as the widely used one by Pan and Tompkins, is then sufficient to accurately detect the R-peaks. The weights are computed for overlapping segments of equal size and the time complexity is shown to be linear in the number of segments. Finally, the method is benchmarked against existing methods using the same thresholding on a noisy and sample accurate database. The results illustrate the potential of the proposed method, which outperforms common detectors by a significant margin.