Ren Yonglian, Liu Feifei, Xia Shengxiang, Shi Shuhua, Chen Lei, Wang Ziyu
School of Science, Shandong Jianzhu University, Jinan, China.
Center for Engineering Computation and Software Development, Shandong Jianzhu University, Jinan, China.
Front Neurosci. 2023 Mar 8;17:1153386. doi: 10.3389/fnins.2023.1153386. eCollection 2023.
Cardiovascular disease is a serious health problem. Continuous Electrocardiograph (ECG) monitoring plays a vital role in the early detection of cardiovascular disease. As the Internet of Things technology continues to mature, wearable ECG signal monitors have been widely used. However, dynamic ECG signals are extremely susceptible to contamination. Therefore, it is necessary to evaluate the quality of wearable dynamic ECG signals. The topological data analysis method (TDA) with persistent homology, which can effectively capture the topological information of high-dimensional data space, has been widely studied. In this study, a brand-new quality assessment method of wearable dynamic ECG signals was proposed based on the TDA with persistent homology method. The point cloud of an ECG signal was constructed, and then the complex sequence was generated and displayed as a persistent barcode. Finally, GoogLeNet based on the transfer learning model with a 10-fold cross-validation method was used to train the classification model. A total of 12-leads ECGs Dataset and single-lead ECGs Dataset, established based on the 2011 PhysioNet/CinC challenge dataset, were both used to verify the performance of this method. In the study, 773 "acceptable" and 225 "unacceptable" signals were used as 12-leads ECGs Dataset. We relabeled 12,000 ECG signals in the challenge dataset, and treated them as single-lead ECGs Dataset after empty lead detection and balance datasets. Compared with the traditional ECG signal quality assessment method mainly based on waveform characteristics and time-frequency characteristics, the performance of the quality assessment method proposed. In this study, the classification performance of the proposed method are fairly great, = 98.04%, 1 = 98.40%, = 97.15%, = 98.93% for 12-leads ECGs Dataset and = 98.55%, 1 = 98.62%, = 98.37%, = 98.85% for single-lead ECGs Dataset.
心血管疾病是一个严重的健康问题。连续心电图（ECG）监测在心血管疾病的早期检测中起着至关重要的作用。随着物联网技术的不断成熟，可穿戴式ECG信号监测器已被广泛使用。然而，动态ECG信号极易受到干扰。因此，有必要评估可穿戴动态ECG信号的质量。具有持久同调的拓扑数据分析方法（TDA）能够有效捕获高维数据空间的拓扑信息，已得到广泛研究。在本研究中，基于具有持久同调方法的TDA提出了一种全新的可穿戴动态ECG信号质量评估方法。构建了ECG信号的点云，然后生成复杂序列并将其显示为持久条形码。最后，使用基于迁移学习模型并采用10折交叉验证方法的GoogLeNet来训练分类模型。基于2011年PhysioNet/CinC挑战赛数据集建立的12导联ECG数据集和单导联ECG数据集均用于验证该方法的性能。在该研究中，773个“可接受”和225个“不可接受”的信号被用作12导联ECG数据集。我们对挑战赛数据集中的12000个ECG信号进行重新标记，并在进行空导联检测和平衡数据集后将它们视为单导联ECG数据集。与主要基于波形特征和时频特征的传统ECG信号质量评估方法相比，本研究中提出的质量评估方法的性能。在本研究中，所提方法的分类性能相当出色，12导联ECG数据集的准确率为98.04%，召回率为98.40%，精确率为97.15%，F1值为98.93%；单导联ECG数据集的准确率为98.55%，召回率为98.62%，精确率为98.37%，F1值为98.85%。
