Quantitative analysis of heart rate variability parameter and mental stress index

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

doi:10.3389/fcvm.2022.930745

Quantitative analysis of heart rate variability parameter and mental stress index

Front Cardiovasc Med. 2022 Jul 25:9:930745. doi: 10.3389/fcvm.2022.930745. eCollection 2022.

Authors

Jiasai Luo¹, Guo Zhang^{1

2}, Yiwei Su¹, Yi Lu¹, Yu Pang¹, Yuanfa Wang¹, Huiqian Wang¹, Kunfeng Cui¹, Yuhao Jiang¹, Lisha Zhong^{1

2}, Zhiwei Huang^{1

2

3

4}

Affiliations

¹ Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
² School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
³ State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China.
⁴ Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China.

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. 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.

Keywords: body area networks; electrocardiogram; heart rate variability; mental stress index; quantitative analysis.