Key result
Improved differential threshold algorithm detects ECG R waves with ~99% accuracy for mental stress assessment.
Why the study?
Quantified heart rate variability indicators combined with wearable devices are important for intelligent telemedicine to assess mental stress in the context of cardiovascular disease as a social health problem.
An improved differential threshold algorithm for R-wave detection achieves >99% recognition rate, enabling quantitative mental stress assessment via HRV on wearable devices.
Answers are grounded in this paper and cite their sources.
May enable wearable HRV-based stress monitoring; leaves open clinical validation in patient cohorts.
Background: Cardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals. Methods: HRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters. Results: Experimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements. Conclusion: Our algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.
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Luo et al. (2022) studied Mental stress. Improved differential threshold R-wave detection algorithm was evaluated on R-wave recognition rate. The improved differential threshold algorithm achieved an R-wave recognition rate of over 99% in real-time ECG data, enabling quantitative assessment of mental stress.
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