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G. Yao, B. Lei; Analyzing ERG Oscillatory Potential in Mouse Using Hilbert-Huang Transform. Invest. Ophthalmol. Vis. Sci. 2007;48(13):1297.
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© ARVO (1962-2015); The Authors (2016-present)
Fourier transform has been applied in ERG signal analysis to extract signal components of particular frequencies such as the oscillatory potentials (OPs). However, conventional Fourier analysis can not derive time-dependent frequency evolution, which may provide valuable information regarding the signal originations. Hilbert-Huang transform is a newly developed algorithm and proven to be effective in analyzing nonstationary and nonlinear signals. We have applied this method to analyze mouse ERGs that present prominent OPs.
Dark- and light-adapted electroretinograms (ERG) were obtained in three mouse strains: wild-type C57BL/6J mouse, cone photoreceptor function loss 1 (cpfl1) mouse, and rhodopsin knockout (rho-/-) mouse. To apply Hilbert-Huang transform, the time domain empirical mode decomposition (EMD) algorithm was used to break down the ERG signals into a series of intrinsic mode functions (IMFs). Then Hilbert transform was applied to derive the time-dependent signal frequencies and amplitudes.
Empirical mode decomposition of mouse ERG usually produced 6-8 intrinsic mode functions. The first 2~3 IMFs contained high frequency signals and were coincident with the temporal locations of OPs. The first IMF has the highest frequency. In dark-adapted C57BL/6J mouse, the first IMF appeared at -3.35 log cd/m2 with a peak amplitude of 15.6µV at 92Hz. It increased to 131.9µV at 110Hz under light intensity of 0.65 log cd/m2. In dark-adapted cpfl1 mouse, the first IMF appeared at -1.85 log cd/m2 with highest amplitude of 11.7µV at 120Hz. It increased to 90.4µV at 120Hz under light intensity of 0.65 log cd/m2. In light-adapted measurements, the C57BL/6J mouse showed 29.3µV peak amplitude at 75Hz at 0.65 log cd/m2 in the first IMF, which was similar to that obtained from rho-/- mouse (26.9µV, 75Hz).
We have applied Hilbert-Huang transform in ERG signal analysis. An advantage of this time domain method is that it obtains time dependent frequency information. The transform may provide a novel method to decipher the ERG signals.
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