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Wavelet feature space in computer-aided electroretinogram evaluation

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Abstract

The paper discusses creating a wavelet-based feature space for the classification of transient pattern electroretinograms (PERGs)—signals utilized in ophthalmology to evaluate the state of the retina. Discrete wavelet transform (DWT) can provide compact signal description, which is more accurate than time-domain data. A procedure for the proper choice of transform parameters is proposed. Both time-domain and wavelet features of these waveforms are visualized using principal components analysis. Separability of feature spaces is compared using k-means clustering algorithm. The results suggest that PERG waveforms are better separable when represented by DWT coefficients of full time-domain signal, than in traditional peak-based feature space.

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Acknowledgements

This paper was supported by a grant from the Polish State Committee for Scientific Research (KBN) number 3T11 023 28.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Szczecin University of Technology, al. Piastów 17, 70-310, Szczecin, Poland

    Tomasz Rogala & Andrzej Brykalski

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  1. Tomasz Rogala
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  2. Andrzej Brykalski
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Correspondence to Tomasz Rogala.

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Rogala, T., Brykalski, A. Wavelet feature space in computer-aided electroretinogram evaluation. Pattern Anal Applic 8, 238–246 (2005). https://doi.org/10.1007/s10044-005-0003-9

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  • DOI: https://doi.org/10.1007/s10044-005-0003-9

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