Estimation of the Power Spectrum of Heart Rate Variability Using Improved Welch Method to Analyze the Degree of Fatigue_Journal of Biomedical Engineering

Authors：

 XUWenhui ¹ , LIUKaihua ¹ , WANGLiting ²

1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;
2. Tsinghua University, Beijing 100084, China;

Corresponding author：

XUWenhui, Email: ljq@seu.edu.cn

Keywords：

heart rate variability; fatigue; power spectrum; Welch; vagal nerve

DOI：

10.7507/1001-5515.20160013

Video：

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Abstract Full text Figures/Tables Video References Cited by

Heart rate variability (HRV) is an important point to judge a person’s state in modern medicine. This paper is aimed to research a person’s fatigue level connected with vagal nerve based on the HRV using the improved Welch method. The process of this method is that it firstly uses a time window function on the signal to be processed, then sets the length of time according to the requirement, and finally makes frequency domain analysis. Compared with classical periodogram method, the variance and consistency of the present method have been improved. We can set time span freely using this method (at present, the time of international standard to measure HRV is 5 minutes). This paper analyses the HRV’s characteristics of fatigue crowd based on the database provided by PhysioNet. We therefore draw the conclusion that the accuracy of Welch analyzing HRV combining with appropriate window function has been improved enormously, and when the person changes to fatigue, the vagal activity is diminished and sympathetic activity is raised.

Citation： XUWenhui, LIUKaihua, WANGLiting. Estimation of the Power Spectrum of Heart Rate Variability Using Improved Welch Method to Analyze the Degree of Fatigue. Journal of Biomedical Engineering, 2016, (1): 67-71. doi: 10.7507/1001-5515.20160013 Copy

1.	ZOCCHI C, ROVETTA A, FANFULLA F. Physiological parameters variation during driving simulations[C]// 2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Zurich: 2007: 1-6.
2.	ZHANG Z X, TIAN X W, LIM J S. New algorithm for the depression diagnosis using HRV:A neuro-fuzzy approach[C]//2011 International Symposium on Bioelectronics and Bioinformatics (ISBB). Suzhou: 2011: 283-286.
3.	TRAN Y, WIJESURIYA N, TARVAINEN M, et al. The relationship between spectral changes in heart rate variability and fatigue[J]. J Psychophysiol, 2009, 23(3): 143-151.
4.	牛杰, 沈晓峰. 疲劳状态监控系统中眼睛状态检测方法[J]. 计算机工程, 2009(17):195-197.
5.	李美俊. 基于步态加速度信号的人体疲劳检测研究[D]. 太原:山西大学, 2012.
6.	MALIK, MAREK. Heart rate variability standards of measurement, physiological interpretation,and clinical use[J]. European Heart Journal, 1996, 17(3): 354-381.
7.	LUCINI D, NORBIATO G, CLERICI M, et al. Hemodynamic and autonomic adjustments to real Life stress conditions in humans[J]. Hypertension, 2002, 39(1): 184-188.
8.	DISHMAN R K, NAKAMURA Y, GARCIA M E, et al. Heart rate variability, trait anxiety, and perceived stress among physically fit men and women[J]. Int J Psychophysiol, 2000, 37(2): 121-133.
9.	FRIEDMAN B H, THAYER J F. Autonomic balance revisited: panic anxiety and heart rate variability[J]. J Psychosom Res, 1998, 44(1): 133-151.
10.	郭玮, 任杰. 脑力疲劳的实验室诱发模型和评价手段研究进展[J]. 中国运动医学杂志, 2013, 32(12):1121-1128.
11.	KAWACHI I, SPARROW D, VOKONAS P S, et al. Symptoms of anxiety and risk of coronary heart disease. The Normative Aging Study[J]. Circulation, 1994, 90(5): 2225-2229.
12.	余训锋，马大玮，魏琳．改进周期图法功率谱估计中的窗函数仿真分析[J]．计算机仿真，2008，25(3)：111-114.
13.	庞兴梅．PhysioNet信息资源解析及利用[J]．医学信息学杂志，2010，31(7)：28-30.
14.	张乾，蒋式勤．PhysioBank数据库及其应用[C]//中国生物医学工程进展--2007中国生物医学工程联合学术年会论文集:下册.2007.
15.	张玉霞．基于MATLAB与WFDB的PhysioBank数据库读取[J]．北京生物医学工程，2011，30(3)：318-320.
16.	董红生．心电波形检测与心率变异性分析方法研究[D]．兰州：兰州理工大学，2012.
17.	HEALEY J A, PICARD R W. Detecting stress during real-world driving tasks using physiological sensors[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(2): 156-166.

1. ZOCCHI C, ROVETTA A, FANFULLA F. Physiological parameters variation during driving simulations[C]// 2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Zurich: 2007: 1-6.
2. ZHANG Z X, TIAN X W, LIM J S. New algorithm for the depression diagnosis using HRV:A neuro-fuzzy approach[C]//2011 International Symposium on Bioelectronics and Bioinformatics (ISBB). Suzhou: 2011: 283-286.
3. TRAN Y, WIJESURIYA N, TARVAINEN M, et al. The relationship between spectral changes in heart rate variability and fatigue[J]. J Psychophysiol, 2009, 23(3): 143-151.
4. 牛杰, 沈晓峰. 疲劳状态监控系统中眼睛状态检测方法[J]. 计算机工程, 2009(17):195-197.
5. 李美俊. 基于步态加速度信号的人体疲劳检测研究[D]. 太原:山西大学, 2012.
6. MALIK, MAREK. Heart rate variability standards of measurement, physiological interpretation,and clinical use[J]. European Heart Journal, 1996, 17(3): 354-381.
7. LUCINI D, NORBIATO G, CLERICI M, et al. Hemodynamic and autonomic adjustments to real Life stress conditions in humans[J]. Hypertension, 2002, 39(1): 184-188.
8. DISHMAN R K, NAKAMURA Y, GARCIA M E, et al. Heart rate variability, trait anxiety, and perceived stress among physically fit men and women[J]. Int J Psychophysiol, 2000, 37(2): 121-133.
9. FRIEDMAN B H, THAYER J F. Autonomic balance revisited: panic anxiety and heart rate variability[J]. J Psychosom Res, 1998, 44(1): 133-151.
10. 郭玮, 任杰. 脑力疲劳的实验室诱发模型和评价手段研究进展[J]. 中国运动医学杂志, 2013, 32(12):1121-1128.
11. KAWACHI I, SPARROW D, VOKONAS P S, et al. Symptoms of anxiety and risk of coronary heart disease. The Normative Aging Study[J]. Circulation, 1994, 90(5): 2225-2229.
12. 余训锋，马大玮，魏琳．改进周期图法功率谱估计中的窗函数仿真分析[J]．计算机仿真，2008，25(3)：111-114.
13. 庞兴梅．PhysioNet信息资源解析及利用[J]．医学信息学杂志，2010，31(7)：28-30.
14. 张乾，蒋式勤．PhysioBank数据库及其应用[C]//中国生物医学工程进展--2007中国生物医学工程联合学术年会论文集:下册.2007.
15. 张玉霞．基于MATLAB与WFDB的PhysioBank数据库读取[J]．北京生物医学工程，2011，30(3)：318-320.
16. 董红生．心电波形检测与心率变异性分析方法研究[D]．兰州：兰州理工大学，2012.
17. HEALEY J A, PICARD R W. Detecting stress during real-world driving tasks using physiological sensors[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(2): 156-166.

Journal of Biomedical Engineering

Estimation of the Power Spectrum of Heart Rate Variability Using Improved Welch Method to Analyze the Degree of Fatigue

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