Journal of Guangdong University of Technology ›› 2019, Vol. 36 ›› Issue (03): 32-38.doi: 10.12052/gdutxb.180161

Previous Articles     Next Articles

A Heart Rate Variability Analysis via Modified Multi-time Scale Permutation Entropy

Lei Rui-sheng, Ling Bingo Wing-Kuen   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2018-12-07 Online:2019-05-09 Published:2019-04-04

HTML

PDF (PC)

2844

Abstract: For analyzing the differences in human heart rate variability under different physiological conditions, and overcoming the problem of analyzing with only single time scale in traditional heart rate variability analysis methods, an HRV analysis method combining the complementary ensemble empirical mode decomposition and modified permutation entropy algorithm is proposed to obtain an indicator named CEEMD-mPE for measuring the obvious differences from HRV time series. The experimental results based on the MIT-BIH arrhythmia database show that our proposed HRV analysis method outperforms the other existing methods based on entropy.

Key words: complementary ensemble empirical mode decomposition, intrinsic mode functions, permutation entropy, heart rate variability

CLC Number: 

  • TP301
[1] SAUL J. Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow[J]. Physiology, 1990, 5(1):32-37
[2] MALIK M, BIGGER J T, CAMM A J, et al. Heart rate variability:Standards of measurement, physiological interpretation, and clinical use[J]. European Heart Journal, 1996, 17(3):354-381
[3] PEÑA M A, ECHEVERRÍA J C, GARCÍA M T, et al. Applying fractal analysis to short sets of heart rate variability data[J]. Medical and Biological Engineering and Computing, 2009, 47(7):709-717
[4] HE L, WANG J, ZHANG L, et al. Decreased fractal dimension of heart rate variability is associated with early neurological deterioration and recurrent ischemic stroke after acute ischemic stroke[J]. Journal of the Neurological Sciences, 2018, 396(November 2018):42-47
[5] CARVAJAL R, WESSEL N, VALLVERDÚ M, et al. Correlation dimension analysis of heart rate variability in patients with dilated cardiomyopathy[J]. Computer Methods and Programs in Biomedicine, 2005, 78(2):133-140
[6] RAWAL K, SAINI B S, SAINI I. Adaptive correlation dimension method for analysing heart rate variability during the menstrual cycle[J]. Australasian Physical and Engineering Sciences in Medicine, 2015, 38(3):509-523
[7] UDHAYAKUMAR R K, KARMAKAR C, PALANISWAMI M. Approximate entropy profile:a novel approach to comprehend irregularity of short-term HRV signal[J]. Nonlinear Dynamics, 2017, 88(2):823-837
[8] CARRICARTE NARANJO C, SANCHEZ-RODRIGUEZ L M, BROWN MARTÍNEZ M, et al. Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy in type 1 diabetes mellitus[J]. Computers in Biology and Medicine, 2017, 86(409):90-97
[9] UDHAYAKUMAR R K, KARMAKAR C, PALANISWAMI M. Understanding irregularity characteristics of short-term HRV signals using sample entropy profile[J]. IEEE Transactions on Biomedical Engineering, IEEE, 2018, 65(11):2569-2579
[10] BOLEA J, BAILÓN R, PUEYO E. On the standardization of approximate entropy:multidimensional approximate entropy index evaluated on short-term HRV time series[EB/OL]. Complexity, 2018.[2019-03-22]. https://doi.org/10.1155/2018/4953273.
[11] YEH J, SHIEH J, HUANG N E. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156
[12] BIAN C, QIN C, MA Q D Y, et al. Modified permutation-entropy analysis of heartbeat dynamics[J]. Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 2012, 85(2):1-7
[13] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. The Royal Society, 1998, 454(1971):903-995
[14] BANDT C, POMPE B. Permutation entropy:a natural complexity measure for time series[J]. Physical Review Letters, 2002, 88(17):174102
[15] MOODY G B, MARK R G. The impact of the MIT-BIH arrhythmia database[J]. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3):45-50
[16] WU Z, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41
[17] GOLDBERGER A L, AMARAL L A, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet:components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23):e215-e220
[18] Association for the Advancement of Medical Instrumentation. Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms[M]. USA:ANSI/AAMI EC38, 1998.
[1] Zhang Rui, Lyu Jun. Single-channel Speech Separation Based on Separated SI-SNR Regression Estimation and Adaptive Frequency Modulation Network [J]. Journal of Guangdong University of Technology, 2023, 40(02): 45-54.
[2] Liu Dong-ning, Wang Zi-qi, Zeng Yan-jiao, Wen Fu-yan, Wang Yang. Prediction Method of Gene Methylation Sites Based on LSTM with Compound Coding Characteristics [J]. Journal of Guangdong University of Technology, 2023, 40(01): 1-9.
[3] Xu Wei-feng, Cai Shu-ting, Xiong Xiao-ming. Visual Inertial Odometry Based on Deep Features [J]. Journal of Guangdong University of Technology, 2023, 40(01): 56-60,76.
[4] Liu Dong-ning, Zheng Chu-chu. Task Allocation under Cool Down Time Constraints via the Many to Many Assignment [J]. Journal of Guangdong University of Technology, 2021, 38(05): 10-15.
[5] Zhang Wei, Tong Ru, Wu Shi-jue, Wang Zi-qi, Teng Shao-hua. Group Role Assignment Based on KD45 Closure [J]. Journal of Guangdong University of Technology, 2021, 38(04): 26-34.
[6] Lyu Shu-yuan, Liu Fu-chun, Zhao Rui, Deng Xiu-qin, Cui Hong-gang. A Research on Patterns Fault Prediction of Decentralized Discrete Event Systems [J]. Journal of Guangdong University of Technology, 2021, 38(01): 54-63.
[7] Hao Zhi-feng, Li Yi-ting, Cai Rui-chu, Zeng Yan, Qiao Jie. A Research on Users’ Shopping Behaviors in Social Network Based on Causal Model [J]. Journal of Guangdong University of Technology, 2020, 37(03): 1-8.
[8] Hong Ying-han, Hao Zhi-feng, Mai Gui-zhen, Chen Ping-hua. Learning Causal Skeleton by Using Lower Order Conditional Independent Tests [J]. Journal of Guangdong University of Technology, 2019, 36(05): 14-19.
[9] Chen He-feng, Wu Nai-qi. Maximally Permissive Supervisor Design Based on Reachablity and Structural Analysis of Petri Net [J]. Journal of Guangdong University of Technology, 2019, 36(04): 1-9.
[10] Shi Cong-cong, Liu Fu-chun. A Pattern-Based Failure Diagnosis of Fuzzy Discrete-Event Systems [J]. Journal of Guangdong University of Technology, 2019, 36(01): 35-41.
[11] Huang Tian-an, Cheng Liang-lun, Huang Si-meng. A Research of Fluctuation Analysis Method on Process Flow of Production Process Based on Internet of Manufacturing Things [J]. Journal of Guangdong University of Technology, 2019, 36(01): 57-62.
[12] Liu Dong-ning, Liu Tong-wu, Song Jing-jing, Hou Yan. Multiple Assignment in Task Allocation of Communication Base Stations [J]. Journal of Guangdong University of Technology, 2018, 35(06): 69-76.
[13] Ye Bin-bin, Liu Fu-chun. Failure Predictability of Stochastic Discrete Event Systems [J]. Journal of Guangdong University of Technology, 2018, 35(06): 83-89.
[14] Zheng San-qiang, Han Xiao-zhuo. A Simulation of Cellular Automata Based on the SIR Infectious Disease Model with Multifactorial Constraints [J]. Journal of Guangdong University of Technology, 2018, 35(05): 51-59.
[15] Zhou Yi-lu, Wang Zhen-you, Li Ye-zi, Li Feng. A Quadratic Scalarizing Function in MOEA/D and its Performance on Multi and Many-Objective Optimization [J]. Journal of Guangdong University of Technology, 2018, 35(04): 37-44.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech