Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM.
Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM.
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使用小波散射和 LSTM 进行可穿戴心电图质量评估
DOI：
10.3389/fphys.2022.905447
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发表时间：
2022
影响因子：
4
通讯作者：
Liu, Chengyu
中科院分区：
文献类型：
--
作者：
Liu, Feifei;Xia, Shengxiang;Wei, Shoushui;Chen, Lei;Ren, Yonglian;Ren, Xiaofei;Xu, Zheng;Ai, Sen;Liu, Chengyu
关键词：
随着可穿戴设备和物联网技术的快速发展，心电信号的实时监测对于心血管疾病的治疗至关重要。然而，在自由生活条件下记录的动态心电信号遭受了极其严重的噪声污染。目前，大多数用于ECG信号评估的算法被设计为将信号分为可接受和不可接受。这种分类不足以进行实时心血管疾病监测。本研究建立了一个包含50,085个记录的可穿戴心电质量数据库，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CVD检测; B：轻度污染信号可用于心率提取; C：重度污染信号需要丢弃）。提出了一种基于三层小波散射网络和迁移学习LSTM的SQA分类方法，通过对信号进行全面深入的分析，提取出更系统、更全面的特征。实验结果（mACC = 98.56%，mF 1 = 98.55%，Se A = 97.90%，Se B = 98.16%，Se C = 99.60%，+ P A = 98.52%，+ P B = 97.60%，+ P C = 99.54%，F 1 A = 98.20%，F 1 B = 97.90%，F1 C = 99.60%）和真实的数据验证表明，该方法具有较高的精度、鲁棒性和计算效率。具有长期动态心电信号质量评价能力。通过去除污染信号并选择高质量的信号段用于进一步分析，有利于促进心血管疾病监测。
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 ( mACC = 98.56%, mF 1 = 98.55%, Se A = 97.90%, Se B = 98.16%, Se C = 99.60%, + P A = 98.52%, + P B = 97.60%, + P C = 99.54%, F 1A = 98.20%, F 1B = 97.90%, F 1C = 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.
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