The proposed signal quality assessment method accepted 97.9% of good and rejected 96.4% of poor quality ECGs, while the lightweight QRS detector achieved an F1 score >99.5% for clean ECGs.
A novel IoT-based wearable ECG SmartVest system demonstrates high accuracy in signal quality assessment and QRS detection, enabling reliable continuous ECG monitoring.
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 F₁ score higher than 99. 5% for processing clean ECGs. Meanwhile, it reports significantly higher F₁ 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.
Liu et al. (Mon,) conducted a other in Cardiovascular diseases. IoT-based wearable 12-lead ECG SmartVest system with SQA and lightweight QRS detector vs. Existing QRS detectors was evaluated on Signal quality assessment accuracy and QRS detection F1 score. The proposed signal quality assessment method accepted 97.9% of good and rejected 96.4% of poor quality ECGs, while the lightweight QRS detector achieved an F1 score >99.5% for clean ECGs.