Skip to main content

Advertisement

SpringerLink
Log in

A novel method for electroretinogram assessment in patients with central retinal vein occlusion

  • Original Research Article
  • Published:
Documenta Ophthalmologica Aims and scope Submit manuscript

Abstract

Purpose

Central retinal vein occlusion (CRVO) is the second most common retinal vascular disorder after diabetic retinopathy that affects the eyes. We propose a method for distinction of normal and central CRVO eyes based on electroretinogram (ERG).

Methods

Seventeen patients with CRVO in one eye were analyzed. Their ERG signals were collected in six different stimuli, including four records in the darkness (dark-adapted 0.01, dark-adapted 3.0, dark-adapted oscillatory potentials, and dark-adapted 10) and two records in brightness (light-adapted 3.0 and light-adapted 30 Hz flicker). Nonlinear features such as Hurst exponent (HE) and approximate entropy (ApEn) were extracted from healthy and CRVO eyes. Finally, a parabolic mapping and two criteria (theta angle and the density of points) were proposed to distinguish the groups.

Results

For ApEn, the P values of dark-adapted 3.0 oscillatory (P = 0.0433) and flicker (P = 0.0425) confirmed significant differences between the groups. For HE, the P values of dark-adapted 3.0 oscillatory (P = 0.0421) and flicker 30 Hz (P = 0.0402) confirmed differences between the healthy and CRVO groups. The P values of theta angle for dark-adapted 3.0 (P = 0.0199), dark-adapted oscillatory (P = 0.0265), dark-adapted 10.0 (P = 0.0166), light-adapted 3.0 (P = 0.0411), and flicker (P = 0.0361) showed significant differences. Using the density criterion, the statistical test demonstrated a significant difference between the groups in dark-adapted 3 (P = 0.0038), dark-adapted oscillatory (P = 0.0102), dark-adapted 10.0 (P = 0.0071), light-adapted 3.0 (P = 0.0319), and flicker 30 Hz (P = 0.0076).

Conclusion

The proposed features have made it possible to distinguish between healthy and CRVO eyes. This method could be helpful in some cases with no definite diagnosis or to estimate the severity of CRVO.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Abbreviations

ApEn:

Approximate entropy

BRVO:

Branch retinal vein occlusion

CRD:

Cone-rod dystrophy

CRVO:

Central retinal vein occlusion

CSNB:

Congenital stationary night blindness

ECG:

Electrocardiogram

EEG:

Electroencephalogram

ERG:

Electroretinogram

ERP:

Early receptor potential

FA:

Fourier analysis

HE:

Hurst exponent

HRVO:

Hemiretinal vein occlusion

ISCEV:

Clinical electrophysiology of vision

LRP:

Late receptor potential

PCA:

Principal component analysis

RP:

Retinitis pigmentosa

RPE:

Retinal pigment epithelium

RVO:

Retinal vessel obstruction

SD:

Standard deviation

WA:

Wavelet transform

References

  1. Uhumwangho OM, Itina EI (2015) Retinal diseases in a tertiary hospital in Southern Nigeria. J West Afr Coll Surg 5(2):1–16

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Wittström E (2017) Central retinal vein occlusion in younger Swedish adults: case reports and review of the literature. Open Ophthalmol J 11:89–102

    PubMed  PubMed Central  Google Scholar 

  3. Rogers S, McIntosh RL, Cheung N et al (2010) The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia. Ophthalmology 117:313–319

    PubMed  PubMed Central  Google Scholar 

  4. Prisco D, Marcucci R (2002) Retinal vein thrombosis: risk factors, pathogenesis, and therapeutic approach. Pathophysiol Haemost Thromb 32(5–6):308–311

    PubMed  Google Scholar 

  5. Lahey JM, Tunc M, Kearney J et al (2002) Laboratory evaluation of hypercoagulable states in patients with central retinal vein occlusion who are less than 56 years of age. Ophthalmology 109(1):126–131

    PubMed  Google Scholar 

  6. Hayreh SS, Klugman MR, Podhajsky P, Kolder HE (1989) Electroretinography in central retinal vein occlusion: correlation of electroretinographic changes with pupillary abnormalities. Graefes Arch Clin Exp Ophthalmol 227(6):549–561

    CAS  PubMed  Google Scholar 

  7. Williamson TH, Keating D, Bradnam M (1997) Electroretinography of central retinal vein occlusion under scotopic and photopic conditions: what to measure? Acta Ophthalmol Scand 75(1):48–53

    CAS  PubMed  Google Scholar 

  8. Larsson J, Andreasson S, Bauer B (1998) Cone b-wave implicit time as an early predictor of rubeosis in central retinal vein occlusion. Am J Ophthalmol 125(2):247–249

    CAS  PubMed  Google Scholar 

  9. Larsson J, Bauer B, Andréasson S (2000) The 30-Hz flicker cone ERG for monitoring the early course of central retinal vein occlusion. Acta Ophthalmol Scand 78(2):187–190

    CAS  PubMed  Google Scholar 

  10. Montzka DP, Brucker AJ, Quinn GE (1991) Electroretinogram interpretation in central retinal vein occlusion. Ophthalmology 98(12):1837–1844

    PubMed  Google Scholar 

  11. Sakaue H, Katsumi O, Hirose T (1989) Electroretinographic findings in fellow eyes of patients with central retinal vein occlusion. Arch Ophthalmol 107(10):1459–1462

    CAS  PubMed  Google Scholar 

  12. Johnson MA, Marcus S, Elman MJ et al (1988) Neovascularization in central retinal vein occlusion: electroretinographic findings. Arch Ophthalmol 106(3):348–352

    CAS  PubMed  Google Scholar 

  13. Hassan-Karimi H, Jafarzadehpur E, Blouri B, Hashemi H, Sadeghi AZ, Mirzajani A (2012) Frequency domain electroretinography in retinitis pigmentosa versus normal eyes. J Ophthalmic Vis Res 7(1):34–38

    PubMed  PubMed Central  Google Scholar 

  14. Gauvin M, Lina J-M, Lachapelle P (2014) Advance in ERG analysis: from peak time and amplitude to frequency, power, and energy. BioMed Res Int, Article ID, p 246096

    Google Scholar 

  15. Nair SS, Joseph KP (2014) Chaotic analysis of the electroretinographic signal for diagnosis. BioMed Res Int, Article ID, p 503920

    Google Scholar 

  16. Bornschein H, Goodman G, Gunkel RD (1957) Temporal aspects of the human electroretinogram: a study of the implicit time-amplitude relationship of the b-wave. AMA Arch Ophthalmol 57(3):386–392

    CAS  PubMed  Google Scholar 

  17. Barraco R, Bellamonte L, Brai M (2007) Time-frequency behavior of the a-wave of the human electroretinogram. IFMBE proceedings 16:919–922

    Google Scholar 

  18. Barraco R, Persano Adorno D, Brai M, Tranchina L (2014) A comparison among different techniques for human ERG signals processing and classification. Phys Med 30:86–95

    CAS  PubMed  Google Scholar 

  19. Miguel JM, Boquete L, Ortega S, Cordero CA, Barea R, Blanco R (2012) MfERG LAB: software for processing multifocal electroretinography signals. Comput Methods Programs Biomed 108(1):377–387

    CAS  PubMed  Google Scholar 

  20. Miguel Jimenez JM, Ortega S, Boquete L, Rodríguez-Ascariz JM, Blanco R (2011) Multifocal ERG wavelet packet decomposition applied to glaucoma diagnosis. BioMed Eng Online 10(37):37

    PubMed  PubMed Central  Google Scholar 

  21. Nair SS, Paul Joseph K (2014) Wavelet-based electroretinographic signal analysis for diagnosis. Biomed Signal Process Control 9:37–44

    Google Scholar 

  22. Crevier DW, Meister M (1998) Synchronous period-doubling in flicker vision of salamander and man. J Neurophysiol 79(4):1869–1878

    CAS  PubMed  Google Scholar 

  23. Molaie M, Falahian R, Gharibzadeh S, Jafari S, Sprott JC (2014) Artificial neural networks: powerful tools for modeling chaotic behavior in the nervous system. Front Comput Neurosci 8(40):40

    PubMed  PubMed Central  Google Scholar 

  24. McCulloch DL, Marmor MF, Brigell MG, Hamilton R, Holder GE, Tzekov R, Bach M (2015) ISCEV Standard for full-field clinical electroretinography. Doc Ophthalmol 130:1–12

    PubMed  Google Scholar 

  25. Marmor MF, Fulton AB, Holder GE, Miyake Y, Brigell M, Bach M (2009) ISCEV Standard for full-field clinical electroretinography (2008 update). Doc Ophthalmol 118:69–77

    CAS  PubMed  Google Scholar 

  26. Singh A, Kaur J (2016) Approximate entropy (ApEn) based heart rate variability analysis. Indian J Sci Technol 9(47):1–4

    Google Scholar 

  27. Kamath C (2015) Entropy measures of irregularity and complexity for surface electrocardiogram time series in patients with congestive heart failure. J Adv Comput Res 6(4):1–11

    Google Scholar 

  28. Natarajan K, Acharya RU, Alias F, Tiboleng T, Puthusserypady SK (2004) Nonlinear analysis of EEG signals at different mental states. BioMed Eng OnLine 3:7

    PubMed  PubMed Central  Google Scholar 

  29. Blythe DAJ, Haufe S, Müller K-R, Nikulin VV (2014) The effect of linear mixing in the EEG on Hurst exponent estimation. NeuroImage 99:377–387

    PubMed  Google Scholar 

  30. Adeli H, Dastidar SG, Dadmehr N (2007) A wavelet-chaos methodology for the analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Trans Biomed Eng 54(2):205–211

    PubMed  Google Scholar 

  31. Sullivan G, Feinn R (2012) Using effect size-or why the p value is not enough. J Grad Med Educ 4(3):279–282

    PubMed  PubMed Central  Google Scholar 

  32. Kannathal N, Chee J, Er K, Lim K, Tat OH (2014) Chaotic analysis of epileptic EEG signals. In: Proceeding of the 15th international conference on biomedical engineering, pp 652–654

  33. Behbahani S, Moridani MK (2015) Nonlinear Poincaré analysis of respiratory. Bratisl Med J 116(7):426–432

    CAS  Google Scholar 

  34. Moridani MK, Pouladian M (2009) Detection ischemic episodes from electrocardiogram signal using wavelet transform. J Biomed Sci Eng 2(04):239

    Google Scholar 

  35. Behbahani S, Nasrabadi AM (2009) Applications of fuzzy similarity index method in the processing of hypnosis. J Biomed Sci Eng 2(5):359–362

    Google Scholar 

  36. Behbahani S, Nasrabadi AM (2013) The relation of susceptibility levels of hypnosis and different mental tasks. Signal Image Video Process 7(4):1–9

    Google Scholar 

  37. Behbahani S, Dabanloo NJ, Nasrabadi AM, Dourado A (2016) Prediction of epileptic seizures based on heart rate variability. Technol Health Care 24(6):795–810

    PubMed  Google Scholar 

  38. Behbahani S, Dabanloo NJ, Nasrabadi AM, Dourado A (2016) Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy. Technol Health Care 23(1):43–54

    Google Scholar 

  39. Matsui Y, Katsumi O, Sakaue H, Hirose T (1994) Electroretinogram b/a wave ratio improvement in central retinal vein obstruction. Br J Ophthalmol 78:191–198

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Breton ME, Schueller AW, Montzka DP (1991) Electroretinogram b-wave implicit time and b/a wave ratio as a function of intensity in central retinal vein occlusion. Ophthalmology 98(12):1845–1853

    CAS  PubMed  Google Scholar 

  41. Yasuda S, Kachi S, Kondo M, Ushida H, Uetani R, Terui T, Piao C-H, Terasaki H (2011) Significant correlation between electroretinogram parameters and ocular vascular endothelial growth factor concentration in central retinal vein occlusion eyes. Investig Ophthalmol Vis Sci 52:5737–5742

    Google Scholar 

  42. Sabates R, Hirose T, McMeel JW (1983) Electroretinography in the prognosis and classification of central retinal vein occlusion. Arch Ophthalmol 101(2):232–235

    CAS  PubMed  Google Scholar 

  43. Ferman-Attar G, Rotenstreich Y (2011) The diagnostic significance of the ERG a/b ratio in retinal disorders. Investig Ophthalmol Vis Sci 52:6073

    Google Scholar 

Download references

Acknowledgements

We are grateful to the Ophthalmology Research Center of Shahid Beheshti University for collaborating on classifying and reviewing patients’ clinical information, as well as monitoring database registration. Also, we would like to thank Ms. Hamideh Sabbaghi for coordination with patients and recording the ERG signals.

Funding

Ophthalmology Research Center of Shahid Beheshti University funded this work.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Neda Sefandarmaz

  2. Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran

    Soroor Behbahani

  3. Ophthalmic Epidemiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Alireza Ramezani

Authors
  1. Neda Sefandarmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soroor Behbahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alireza Ramezani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soroor Behbahani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of human rights

All procedures performed in studies involving human participants were by the ethical standards of the (Ethics Committee of Ophthalmic Research Center, Shahid Beheshti Medical University (Tehran, Iran)) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Statement on welfare of animal

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sefandarmaz, N., Behbahani, S. & Ramezani, A. A novel method for electroretinogram assessment in patients with central retinal vein occlusion. Doc Ophthalmol 140, 257–271 (2020). https://doi.org/10.1007/s10633-019-09742-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10633-019-09742-2

Keywords

Search

Navigation