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    Fractal Dimension Analysis of Transient Visual Evoked Potentials: Optimisation and Applications

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    Author
    Boon, MY; Henry, BI; Chu, BS; Basahi, N; Suttle, CM; Luu, C; Leung, H; Hing, S
    Date
    2016-09-06
    Source Title
    PLoS One
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Luu, Chi; Luu, Chi
    Affiliation
    Ophthalmology (Eye & Ear Hospital)
    Centre for Eye Research Australia (CERA)
    Metadata
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    Document Type
    Journal Article
    Citations
    Boon, M. Y., Henry, B. I., Chu, B. S., Basahi, N., Suttle, C. M., Luu, C., Leung, H. & Hing, S. (2016). Fractal Dimension Analysis of Transient Visual Evoked Potentials: Optimisation and Applications. PLOS ONE, 11 (9), https://doi.org/10.1371/journal.pone.0161565.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256653
    DOI
    10.1371/journal.pone.0161565
    Abstract
    PURPOSE: The visual evoked potential (VEP) provides a time series signal response to an external visual stimulus at the location of the visual cortex. The major VEP signal components, peak latency and amplitude, may be affected by disease processes. Additionally, the VEP contains fine detailed and non-periodic structure, of presently unclear relevance to normal function, which may be quantified using the fractal dimension. The purpose of this study is to provide a systematic investigation of the key parameters in the measurement of the fractal dimension of VEPs, to develop an optimal analysis protocol for application. METHODS: VEP time series were mathematically transformed using delay time, τ, and embedding dimension, m, parameters. The fractal dimension of the transformed data was obtained from a scaling analysis based on straight line fits to the numbers of pairs of points with separation less than r versus log(r) in the transformed space. Optimal τ, m, and scaling analysis were obtained by comparing the consistency of results using different sampling frequencies. The optimised method was then piloted on samples of normal and abnormal VEPs. RESULTS: Consistent fractal dimension estimates were obtained using τ = 4 ms, designating the fractal dimension = D2 of the time series based on embedding dimension m = 7 (for 3606 Hz and 5000 Hz), m = 6 (for 1803 Hz) and m = 5 (for 1000Hz), and estimating D2 for each embedding dimension as the steepest slope of the linear scaling region in the plot of log(C(r)) vs log(r) provided the scaling region occurred within the middle third of the plot. Piloting revealed that fractal dimensions were higher from the sampled abnormal than normal achromatic VEPs in adults (p = 0.02). Variances of fractal dimension were higher from the abnormal than normal chromatic VEPs in children (p = 0.01). CONCLUSIONS: A useful analysis protocol to assess the fractal dimension of transformed VEPs has been developed.

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