AUTHOR=Luo Jiasai , Zhang Guo , Su Yiwei , Lu Yi , Pang Yu , Wang Yuanfa , Wang Huiqian , Cui Kunfeng , Jiang Yuhao , Zhong Lisha , Huang Zhiwei 

TITLE=Quantitative analysis of heart rate variability parameter and mental stress index

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.930745

DOI=10.3389/fcvm.2022.930745

ISSN=2297-055X

ABSTRACT=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. In order to quantify the changes of human mental state, this paper 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 improved by improving the starting position of R wave and the time window function of the traditional differential threshold method. The experimental platform in this paper 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 shows that the recognition rate of R wave of real-time 5min ECG data collected by this algorithm is more than 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 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.