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Analysis of bioelectrical signals of the human retina (PERG) and visual cortex (PVEP) evoked by pattern stimuli

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
Purpose: to demonstrate the possibility of finding features reliable for more precise distinguishing between normal and abnormal Pattern Electroretinogram (PERG) recordings, in Continuous Wavelet Transform (CWT) coefficients domain. To determine characteristic features of the PERG and Pattern Visual Evoked Potential (PVEP) waveforms important in the task of precise classification and assessment of these recordings. Material and methods: 60 normal PERG waveforms and 60 PVEPs as well as 47 PERGs and 27 PVEPs obtained in some retinal and optic nerve diseases were studied in the two age groups (< 50 years, > 50 years). All these signals were recorded in accordance with the guidelines of ISCEV in the Laboratory of Electrophysiology of the Retina and Visual Pathway and Static Perimetry, at the Department and Clinic of Ophthalmology of the Pomeranian Medical University. Continuous Wavelet Transform (CWT) was used for the time-frequency analysis and modelling of the PERG signal. Discriminant analysis and logistic regression were performed in statistical analysis of the PERG and PVEP signals. Obtained mathematical models were optimized using Fisher F (nI, n2) test. For preliminary evaluation of the obtained classification methods and algorithms in clinical practice, 22 PERGs and 55 PVEPs were chosen with respect to especially difficult discrimination problems ("borderline" recordings). Results: comparison between the method using CWT and standard time-domain based analysis showed that determining the maxima and minima of the PERG waves was achieved with better accuracy. This improvement was especially evident in waveforms with unclear peaks as well as in noisy signals. Predictive, quantitative models for PERGs and PVEPs binary classification were obtained based on characteristic features of the waveform morphology. Simple calculations algorithms for clinical applications were elaborated. They proved effective in distinguishing between normal and abnormal recordings. Conclusions: CWT based method is efficient in more precise assessment of the latencies of the PERG waveforms, improving separation between normal and abnormal waveforms. Filtering of the PERG signal may be optimized based on the results of the CWT analysis. Classification of the PERG and PVEP waveforms based on statistical methods is useful in preliminary interpretation of the recordings as well as in supporting more accurate assessment of clinical data.
Rocznik
Strony
223--229
Opis fizyczny
Bibliogr. 20 poz., 7 rys., 6 tab.
Twórcy
autor
  • Institute of Electronics, Telecommunication and Information Technology, Szczecin University of Technology 37 Wł. Sikorskiego Str., 70-313 Szczecin, Poland., penkala@ps.pl
Bibliografia
  • [1] G.E. Holder, “Pattern electroretinography (PERG) and integrated approach to visual pathway diagnosis”, Progr. in Retinal and Eye Res. 20 (4), 531–561 (2001).
  • [2] O. Palacz, W. Lubi´nski, and K. Penkala, Clinical Electrophysiology of the Visual System (in Polish). OFTAL, Warszawa, 2003.
  • [3] M.F. Marmor, G.E. Holder, M.W. Seeliger, and S. Yamamoto, “Standard for clinical electroretinography (2004 update)”, Doc. Ophthalmol. 108, 107–114 (2004).
  • [4] M. Bach et al. “Standard for pattern electroretinography”, Doc. Ophthalmol. 101, 11–18 (2000).
  • [5] V. Odom et al., “Visual evoked potentials standard (2004)”, Doc. Ophthalmol. 108, 115–123 (2004).
  • [6] M. Bach and T. Meigen, “Do’s and Don’ts in Fourier analysis of steady-state potentials”, Doc. Ophthalmol. 99, 69–82 (1999).
  • [7] T. Otto and M. Bach, “Retest variability and diurnal effects in the pattern electroretinogram”, Doc. Ophthalmol. 92, 311–323 (1997).
  • [8] W. Lubi´nski, K. Krzystolik, C. Cybulski, Z. Szych, K. Penkala, O. Palacz, and J. Lubi´nski, “Retinal function in the von Hippel- Lindau disease”, Doc. Ophthalmol. 106, 271–280 (2003).
  • [9] M. Akay, Time Frequency and Wavelets in Biomedical Signal Processing, Wiley, 2000.
  • [10] M. Unser and A. Aldroubi, “A review of wavelets in biomedical applications”, Proc. IEEE 84 (4), 626–638 (1996).
  • [11] T. Rogala, A. Brykalski, and K. Penkala, “Certain applications of the wavelet transform in biomedical engineering”, XXVI ICSPETO, Gliwice – Niedzica 2, 415–418 (2003).
  • [12] H. Drissi, F. Regragui, J.P. Antoine, and M. Benouna, “Wavelet transform analysis of visual evoked potentials”, Proc. UCL-IPT Conference, (1998).
  • [13] A.C. Fisher, R.P. Hagan, A. Mackay, and M.C. Brown, “Removal of the frame break-through artefact in PRVEP recordings using a system of wavelet decomposition”, 2nd Annual Meeting of the British Society for Clinical Electrophysiology of Vision (BriSCEV), Liverpool, p.102 (2004).
  • [14] K. Penkala, T. Rogala, and A. Brykalski, “Analysis of the pattern electroretinogram signal using the wavelet transform”, 48 Internationales Wissenschaftliches Kolloquium, Ilmenau, 145–146 (2003).
  • [15] K. Penkala, T. Rogala, A. Brykalski, W. Lubiński, and O. Palacz, “Wavelet approach to the PERG analysis and processing”, 2nd Annual Meeting of the British Society for Clinical Electrophysiology of Vision (BriSCEV), p.103, Liverpool (2004).
  • [16] K. Penkala, T. Rogala, A. Brykalski, andW. Lubiński, “Wavelet transform in analysis of the pattern responses of the human retina (pattern electroretinogram – PERG)”, 4th European Symposium on Biomedical Engineering, Patras, Greece., (2004).
  • [17] T. Rogala, A. Brykalski, and K. Penkala, “Wavelet compression of electroretinogram – preprocessing for neural classification purposes”, Proc. MMAR’04 1, 641–646 (2004).
  • [18] T. Rogala, A. Brykalski, and K. Penkala, “Certain Aspects of bioelectrical signal smoothing” Measurements, Automatics, Control – PAK (9), 21–25 (2004).
  • [19] K. Szlachta, K. Penkala, A. Brykalski, and W. Lubiński, “Statistical analysis of the pattern electroretinogram (PERG) signal”, Proc. 6th International Conference on Unconventional Electromechanical and Electrical Systems UEES’04 3, 927–930 (2004).
  • [20] K. Penkala,W. Lubiński, K. Szlachta, and D. Karczewicz, “Discriminant analysis of PERG and PVEP – mathematical models, preliminary clinical evaluation”, International Society for Clinical Electrophysiology of Vision (ISCEV) XLIII Annual Symposium, Glasgow, 23–27.08.2005 (to be published).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG5-0006-0004
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