College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China.
College of Medicine and Biological Information Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
Sensors (Basel). 2022 Jul 24;22(15):5516. doi: 10.3390/s22155516.
Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW-RQA features. As validation, the CTW-RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW-RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW-RQA features effectively supplement the existing BCG features for AF detection.
心房颤动（AF）是最常见的具有临床意义的心律失常；因此，AF 的检测至关重要。在这里，我们提出了一种新的特征提取方法，使用心冲击图（BCG）来提高 AF 检测性能，BCG 是一种由心肌力产生的体表弱振动信号。在本文中，连续时间窗口（CTW）被添加到每个 BCG 段中，并从每个时间窗口中提取递归量化分析（RQA）特征。然后，讨论了 CTW 的数量，并对来自多个时间窗口的组合特征进行了排名，最终构成了 CTW-RQA 特征。作为验证，从 59 名受试者的 4000 个 BCG 段中提取 CTW-RQA 特征，并与经典的时频特征和最新的能量特征进行比较。所提出特征的准确性更高，融合了三种类型的特征，可获得 95.63%的最高精度。为了评估所提出特征的重要性，使用卡方检验对融合特征进行排名。CTW-RQA 特征占前 10 个融合特征的 60%，占前 17 个融合特征的 65%。因此，所提出的 CTW-RQA 特征有效地补充了现有的 BCG 特征，用于 AF 检测。
