Signal Processing ( IF 4.4 ) Pub Date : 2021-08-13 , DOI: 10.1016/j.sigpro.2021.108288 Wei Li 1 , Hong Wang 1 , Luhe Zhuang 1 , Shu Han 1 , Hui Zhang 1 , Jihua Wang 1
Atrial fibrillation (AF) is a common cause of serious diseases such as stroke, heart failure and coronary artery disease, and electrocardiogram (ECG) detection is an important means of identifying AF. However, the ECG signal is quite noisy, making it difficult to detect AF through this method. Removing noise interference in ECG signals is a challenging problem. Traditional methods usually adopt various filtering methods to tackle this problem. Inspired by complex network theory, in this paper we present an innovative denoising approach for ECG detection called weighted multi-scale limited penetrable visibility graph (WMS-LPVG), which allows us to detect the rhythms characterizing AF in noisy ECG signals. To our knowledge, this is the first model that represents the AF rhythm series from the perspective of multi-scale complex networks. Furthermore, our WMS-LPVG model characterizes the AF rhythms in more detail, enabling us to identify AF sufferers more accurately. To demonstrate the effectiveness of our WMS-LPVG method, we first propose a new concept, called local efficiency entropy (LEE), which is utilized to identify the dynamic characteristics of time series. We then study the LEE-fluctuation trend under different scale factors. The experimental results show that the proposed LEE criterion can identify four kinds of ECG waveforms at a large scale. We then fuse the extracted LEE features with the original sequential features of ECG signals to build a multi-model complex network and feed the fused features into an XGboost model to identify AF patients. To demonstrate the generality of our WMS-LPVG model, we construct complex networks with WMS-LPVG for periodic and chaotic time series, respectively, and further discuss their degree distributions. The results show that our WMS-LPVG method perfectly retains information about original sequences and offers good anti-noise ability.
中文翻译:
用于探索房颤节律的加权多尺度有限可穿透可见性图
心房颤动(AF)是脑卒中、心力衰竭和冠状动脉疾病等严重疾病的常见病因,心电图(ECG)检测是鉴别AF的重要手段。但是,ECG 信号噪声很大,很难通过这种方法检测 AF。去除 ECG 信号中的噪声干扰是一个具有挑战性的问题。传统方法通常采用各种过滤方法来解决这个问题。受复杂网络理论的启发,在本文中,我们提出了一种创新的 ECG 检测去噪方法,称为加权多尺度有限可穿透能见度图 (WMS-LPVG),它使我们能够在嘈杂的 ECG 信号中检测表征 AF 的节律。据我们所知,这是第一个从多尺度复杂网络的角度代表 AF 节律序列的模型。此外,我们的 WMS-LPVG 模型更详细地表征了 AF 节律,使我们能够更准确地识别 AF 患者。为了证明我们的 WMS-LPVG 方法的有效性,我们首先提出了一个新概念,称为局部效率熵(LEE),用于识别时间序列的动态特征。然后我们研究了不同尺度因子下的 LEE 波动趋势。实验结果表明,所提出的LEE准则可以大规模识别四种心电波形。然后我们将提取的 LEE 特征与 ECG 信号的原始序列特征融合以构建多模型复杂网络,并将融合的特征输入 XGboost 模型以识别 AF 患者。为了证明我们的 WMS-LPVG 模型的通用性,我们使用 WMS-LPVG 为周期性和混沌时间序列构建复杂网络,分别,并进一步讨论它们的度分布。结果表明,我们的 WMS-LPVG 方法完美地保留了原始序列的信息,并具有良好的抗噪能力。