Статья

Signal Quality Assessment and Lightweight QRS Detection for Wearable ECG SmartVest System

Chengyu LiuState Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, ChinaXiangyu ZhangState Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, ChinaLina ZhaoSchool of Control Science and Engineering, Shandong University, Jinan, ChinaFeifei LiuState Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, ChinaXingwen ChenLenovo China Empowerment Center, Lenovo, Shenzhen, ChinaYingjia YaoLenovo China Empowerment Center, Lenovo, Beijing, ChinaJianqing LiState Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, the School of Instrument Science and Engineering, Southeast University, Nanjing, China

2018en

ABI

Аннотация

Recently, development of wearable and Internet of Things (IoT) technologies enables the real-time and continuous individual electrocardiogram (ECG) monitoring. In this paper, we develop a novel IoT-based wearable 12-lead ECG SmartVest system for early detection of cardiovascular diseases, which consists of four typical IoT components: 1) sensing layer using textile dry ECG electrode; 2) network layer utilizing Bluetooth, WiFi, etc.; 3) cloud saving and calculation platform and server; and 4) application layer for signal analysis and decision making. We focus on addressing the challenge of real-time signal quality assessment (SQA) and lightweight QRS detection for wearable ECG application. First, a combination method of multiple signal quality indices and machine learning is proposed for classifying 10-s single-channel ECG segments as acceptable and unacceptable. Then a lightweight QRS detector is developed for accurate location of QRS complexes. The results show that the proposed SQA method can efficiently deal with tradeoff between accepting good (97.9%) and rejecting poor (96.4%) quality ECGs, ensuring that only a low percentage of recorded ECGs are discarded. The proposed lightweight QRS detector achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F_{1}}$ </tex-math></inline-formula> score higher than 99.5% for processing clean ECGs. Meanwhile, it reports significantly higher <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F_{1}}$ </tex-math></inline-formula> scores than two existing QRS detectors for processing noisy ECGs. In addition, it also has a fine computation efficiency. This paper demonstrates that the developed IoT-driven ECG SmartVest system can be applied for widely monitoring the population during daily life and has a promising application future.

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Идентификаторы

DOI: 10.1109/jiot.2018.2844090

Цитирования и источники

Цитирований: 3Использованных источников: 0

Показатели — AkademScholar