Skip to main content
Springer Nature Link
Log in

A fast sample entropy for pulse rate variability analysis

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Pernice R, Javorka M, Krohova J, Czippelova B, Turianikova Z, Busacca A, Faes L (2019) Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring. Med Biol Eng Comput 57(6):1247–1263

    Article  PubMed  Google Scholar 

  2. Ishaque S, Khan N, Krishnan S (2021) Trends in heart-rate variability signal analysis. Front Digit Health 3:639444

    Article  PubMed  PubMed Central  Google Scholar 

  3. Saul JP, Valenza G (2021) Heart rate variability and the dawn of complex physiological signal analysis: methodological and clinical perspectives. Philos Trans R Soc A 379(2212):20200255

    Article  Google Scholar 

  4. Ziemssen T, Siepmann T (2019) The investigation of the cardiovascular and sudomotor autonomic nervous system—a review. Front Neurol 10:53

    Article  PubMed  PubMed Central  Google Scholar 

  5. Mejía-Mejía E, May JM, Kyriacou PA (2022) Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography. Comput Methods Programs Biomed 218:106724

    Article  PubMed  Google Scholar 

  6. Jan H-Y, Chen M-F, Fu T-C, Lin W-C, Tsai C-L, Lin K-P (2019) Evaluation of coherence between ECG and PPG derived parameters on heart rate variability and respiration in healthy volunteers with/without controlled breathing. J Med Biol Eng 39(5):783–795

    Article  Google Scholar 

  7. Mejía-Mejía E, May JM, Kyriacou PA (2021) Effect of filtering of photoplethysmography signals in pulse rate variability analysis. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE, pp 5500–5503

  8. Hejjel L, Béres S (2021) Comment on ‘pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability’. Physiol Meas 42(1):018001

    Article  PubMed  Google Scholar 

  9. Chou Y, Gu J, Liu J, Gu Y, Lin J (2019) Bradycardia and tachycardia detection using a synthesis-by-analysis modeling approach of pulsatile signal. IEEE Access 7:131256–131269

    Article  Google Scholar 

  10. Choudhary T, Das M, Sharma LN, Bhuyan MK (2021) Analyzing seismocardiographic approach for heart rate variability measurement. Biomed Sig Process Control 68:102793

    Article  Google Scholar 

  11. Mandal S, Mondal P, Roy AH (2021) Detection of ventricular arrhythmia by using heart rate variability signal and ECG beat image. Biomed Sig Process Control 68:102692

    Article  Google Scholar 

  12. Sluyter JD, Jr CAC, Lowe A, Scragg RKR (2019) Pulse rate variability predicts atrial fibrillation and cerebrovascular events in a large, population-based cohort. Int J Cardiol 275:83–88

  13. Li K, Rüdiger H, Ziemssen T (2019) Spectral analysis of heart rate variability: time window matters. Front Neurol 10:545

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fallet S, Lemay M, Renevey P, Leupi C, Pruvot E, Vesin J-M (2019) Can one detect atrial fibrillation using a wrist-type photoplethysmographic device? Med Biol Eng Comput 57(2):477–487

    Article  PubMed  Google Scholar 

  15. Hao T, Zheng X, Wang H, Xu K, Chen S (2022) Linear and nonlinear analyses of heart rate variability signals under mental load. Biomed Sig Process Control 77:103758

    Article  Google Scholar 

  16. Rohila A, Sharma A (2019) Phase entropy: A new complexity measure for heart rate variability. Physiol Meas 40(10):105006

    Article  PubMed  Google Scholar 

  17. Richman JS, Lake DE, Moorman RJ (2004) Sample entropy. In: Methods in enzymology, vol 384. Elsevier, pp 172–184

  18. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart Circ Physiol 278(6):H2039–H2049

    Article  CAS  PubMed  Google Scholar 

  19. Porta A, Bari V, Maria BD, Cairo B, Vaini E, Malacarne M, Pagani M, Lucini D (2018) On the relevance of computing a local version of sample entropy in cardiovascular control analysis. IEEE Trans Biomed Eng 66(3):623–631

    Article  PubMed  Google Scholar 

  20. Porta A, Gnecchi-Ruscone T, Tobaldini E, Guzzetti S, Furlan R, Montano N (2007) Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. J Appl Physiol 103(4):1143–1149

    Article  PubMed  Google Scholar 

  21. Buszko K, Piatkowska A, Koźluk E, Fabiszak T, Opolski G (2018) Entropy measures in analysis of head up tilt test outcome for diagnosing vasovagal syncope. Entropy 20(12):976

    Article  PubMed  PubMed Central  Google Scholar 

  22. Javorka M, Trunkvalterova Z, Tonhajzerova I, Javorkova J, Javorka K, Baumert M (2008) Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus. Clin Neurophysiol 119(5):1071–1081

    Article  PubMed  Google Scholar 

  23. Álvarez D, Sánchez-Fernández A, Andrés-Blanco AM, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Barroso-García V, Hornero R, Del Campo F (2019) Influence of chronic obstructive pulmonary disease and moderate-to-severe sleep apnoea in overnight cardiac autonomic modulation: Time, frequency and non-linear analyses. Entropy 21(4):381

    Article  PubMed  PubMed Central  Google Scholar 

  24. Chou Y, Zhu P, Huang X, Lin J, Liu J, Gu Y (2018) Comparison between heart rate variability and pulse rate variability for bradycardia and tachycardia subjects. In: 2018 international conference on control, automation and information sciences (ICCAIS). IEEE, pp 1–6

  25. Vaquerizo-Villar F, Álvarez D, Kheirandish-Gozal L, Gutiérrez-Tobal GC, Barroso-García V, Crespo A, Del Campo F, Gozal D, Hornero R (2018) Detrended fluctuation analysis of the oximetry signal to assist in paediatric sleep apnoea–hypopnoea syndrome diagnosis. Physiol Meas 39(11):114006

    Article  PubMed  Google Scholar 

  26. Nguyen QDN, Liu A-B, Lin C-W (2020) Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers. Entropy 22(12):1340

    Article  Google Scholar 

  27. Wang Z, Yao L, Cai Y (2020) Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine. Measurement 156:107574

    Article  Google Scholar 

  28. Shang Y, Lu G, Kang Y, Zhou Z, Duan B, Zhang C (2020) A multi-fault diagnosis method based on modified sample entropy for lithium-ion battery strings. J Power Sources 446:227275

    Article  CAS  Google Scholar 

  29. Goldberger AL, Amaral Luis AN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  CAS  PubMed  Google Scholar 

  30. Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA (1996) Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol-Regul Integr Comp Physiol 271 (4):R1078–R1084

    Article  CAS  Google Scholar 

  31. Chou Y, Zhang A, Gu J, Liu J, Gu Y (2020) A recognition method for extreme bradycardia by arterial blood pressure signal modeling with curve fitting. Physiol Meas 41(7):074002

    Article  PubMed  Google Scholar 

  32. Chou Y, Zhang A, Wang P, Gu J (2014) Pulse rate variability estimation method based on sliding window iterative DFT and Hilbert transform. J Med Biol Eng 34(4):347–355

    Article  Google Scholar 

  33. Parbat D, Chakraborty M (2021) A novel methodology to study the cognitive load induced EEG complexity changes: Chaos, fractal and entropy based approach. Biomed Sig Process Control 64:102277

    Article  Google Scholar 

  34. Udhayakumar RK, Karmakar C, Palaniswami M (2018) Understanding irregularity characteristics of short-term HRV signals using sample entropy profile. IEEE Trans Biomed Eng 65(11):2569–2579

    Article  PubMed  Google Scholar 

  35. Manis G, Md A, Sassi R (2018) Low computational cost for sample entropy. Entropy 20 (1):61

    Article  PubMed  PubMed Central  Google Scholar 

  36. Fan Z, Dong S, Chi J, Zhuang X, Mastorakis NE (2018) A fast algorithm of correlation dimension estimation for nonlinear time series. pp 595–597

  37. Chou L, Zhang K, Liu J, Gong S, Chou Y (2021) Life-threatening arrhythmias recognition by pulse-to-pulse intervals analysis. Int J Comput Appl Technol 67(2-3):185–193

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (grant 61901062), and the Outstanding Young Backbone Teachers of the ‘Blue Project’ in Colleges and Universities.

Author information

Authors and Affiliations

  1. School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China

    Lijuan Chou, Haiping Yang, Jicheng Liu & Yongxin Chou

  2. School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China

    Lijuan Chou & Shengrong Gong

  3. School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China

    Shengrong Gong

Authors
  1. Lijuan Chou
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shengrong Gong
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haiping Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jicheng Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yongxin Chou
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yongxin Chou.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chou, L., Gong, S., Yang, H. et al. A fast sample entropy for pulse rate variability analysis. Med Biol Eng Comput 61, 1603–1617 (2023). https://doi.org/10.1007/s11517-022-02766-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-022-02766-y

Keywords