Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:121-126. doi: 10.1109/EMBC48229.2022.9871266.
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.
自 Einthoven 奠定现代心电图学的基础以来，已经过去了一个多世纪。近年来，随着可穿戴和低成本设备的进步，对嘈杂 ECG 信号中 R 波峰的准确检测变得越来越重要。为了满足这些需求，我们提出了一种新的 R 波峰检测方法，该方法基于可见性图变换，通过将每个样本表示为一个节点，并为可见样本之间分配边，将离散时间序列映射到图中。所提出的方法利用大的、孤立的值的高连通性来加权原始信号，从而放大 R 波峰，同时抑制其他信号分量和噪声。然后，仅使用简单的阈值处理程序（例如 Pan 和 Tompkins 广泛使用的程序）就足以准确地检测 R 波峰。权重是为大小相等的重叠段计算的，时间复杂度与段数呈线性关系。最后，使用相同的阈值在嘈杂且样本准确的数据库上对现有方法进行基准测试。结果表明，该方法具有潜力，其性能明显优于常见的检测器。
