Liu Feifei, Xia Shengxiang, Wei Shoushui, Chen Lei, Ren Yonglian, Ren Xiaofei, Xu Zheng, Ai Sen, Liu Chengyu
School of Science, Shandong Jianzhu University, Jinan, China.
School of Control Science and Engineering, Shandong University, Jinan, China.
Front Physiol. 2022 Jun 30;13:905447. doi: 10.3389/fphys.2022.905447. eCollection 2022.
As the fast development of wearable devices and Internet of things technologies, real-time monitoring of ECG signals is quite critical for cardiovascular diseases. However, dynamic ECG signals recorded in free-living conditions suffered from extremely serious noise pollution. Presently, most algorithms for ECG signal evaluation were designed to divide signals into acceptable and unacceptable. Such classifications were not enough for real-time cardiovascular disease monitoring. In the study, a wearable ECG quality database with 50,085 recordings was built, including A/B/C (or high quality/medium quality/low quality) three quality grades (A: high quality signals can be used for CVD detection; B: slight contaminated signals can be used for heart rate extracting; C: heavily polluted signals need to be abandoned). A new SQA classification method based on a three-layer wavelet scattering network and transfer learning LSTM was proposed in this study, which can extract more systematic and comprehensive characteristics by analyzing the signals thoroughly and deeply. Experimental results ( = 98.56%, = 98.55%, = 97.90%, = 98.16%, = 99.60%, + = 98.52%, + = 97.60%, + = 99.54%, = 98.20%, = 97.90%, = 99.60%) and real data validations proved that this proposed method showed the high accuracy, robustness, and computationally efficiency. It has the ability to evaluate the long-term dynamic ECG signal quality. It is advantageous to promoting cardiovascular disease monitoring by removing contaminating signals and selecting high-quality signal segments for further analysis.
随着可穿戴设备和物联网技术的快速发展，心电图（ECG）信号的实时监测对于心血管疾病至关重要。然而，在自由生活条件下记录的动态ECG信号受到极其严重的噪声污染。目前，大多数用于ECG信号评估的算法旨在将信号分为可接受和不可接受两类。这种分类对于实时心血管疾病监测是不够的。在本研究中，构建了一个包含50085条记录的可穿戴ECG质量数据库，包括A/B/C（或高质量/中等质量/低质量）三个质量等级（A：高质量信号可用于心血管疾病检测；B：轻度污染信号可用于心率提取；C：严重污染信号需要舍弃）。本研究提出了一种基于三层小波散射网络和迁移学习长短期记忆网络（LSTM）的新的信号质量评估（SQA）分类方法，该方法可以通过深入透彻地分析信号来提取更系统、更全面的特征。实验结果（召回率=98.56%，精确率=98.55%，F1值=97.90%，马修斯相关系数=98.16%，准确率=99.60%，灵敏度+特异度=98.52%，阳性预测值+阴性预测值=97.60%，综合判别指标=99.54%，错误发现率=98.20%，假阳性率=97.90%，阴性预测值=99.60%）和实际数据验证证明，该方法具有很高的准确性、鲁棒性和计算效率。它有能力评估长期动态ECG信号质量。通过去除污染信号并选择高质量信号段进行进一步分析，有利于促进心血管疾病监测。
