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EN
In the paper a short review of methods applied for pattern electroretinogram signal analysis is presented. Various possible alternatives for classical method used in medical practice are described. The capabilities and disadvantages of each method as well as relevant results are briefly presented and/or references are cited. The described algorithms are: statistical regression analysis, continuous wavelet transform, discrete wavelet transform, artificial neural networks, principal components analysis and independent component analysis. The aim of the paper is to give a short review of previously taken activity in the field of pattern electroretinogram analysis particularly for diagnostic purposes, and present a guide for possible approaches to be applied for other bioelectrical signals.
PL
W artykule przedstawiono przegląd metod zastosowanych do analizy sygnału elektroretinogramu wywołanego wzorcem. Zaprezentowano szereg możliwych technik alternatywnych w stosunku do procedur używanych w praktyce klinicznej. Przedyskutowano zalety i ograniczenia każdego z algorytmów, przedstawiając pokrótce wyniki doświadczeń lub cytując odpowiednie pozycje literatury. Opisane algorytmy to: statystyczna analiza regresji, ciągła i dyskretna transformata falkowa, sztuczne sieci neuronowe, analiza składowych głównych (PCA) oraz analiza składowych niezależnych (ICA). Celem niniejszego artykuły jest usystematyzowanie wcześniejszych działań autorów w dziedzinie analizy elektroretinogramu wywołanego wzorcem, w szczególności dla potrzeb diagnostyki, oraz zaproponowanie metodologii badań sygnałów bioelektrycznych o podobnym charakterze.
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.
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