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

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.