Title

SWDreader: A wavelet-based algorithm using spectral phase to characterize spike-wave morphological variation in genetic models of absence epilepsy.

Document Type

Article

Publication Date

3-15-2015

JAX Source

J Neurosci Methods 2015 Mar 15; 242:127-40.

PMID

25549550

Abstract

BACKGROUND: Spike-wave discharges (SWD) found in neuroelectrical recordings are pathognomonic to absence epilepsy. The characteristic spike-wave morphology of the spike-wave complex (SWC) constituents of SWDs can be mathematically described by a subset of possible spectral power and phase values. Morlet wavelet transform (MWT) generates time-frequency representations well-suited to identifying this SWC-associated subset.

NEW METHOD: MWT decompositions of SWDs reveal spectral power concentrated at harmonic frequencies. The phase relationships underlying SWC morphology were identified by calculating the differences between phase values at SWD fundamental frequency from the 2nd, 3rd, and 4th harmonics, then using the three phase differences as coordinates to generate a density distribution in a {360°×360°×360°} phase difference space. Strain-specific density distributions were generated from SWDs of mice carrying the Gria4, Gabrg2, or Scn8a mutations to determine whether SWC morphological variants reliably mapped to the same regions of the distribution, and if distribution values could be used to detect SWD.

COMPARISON WITH EXISTING METHODS: To the best of our knowledge, this algorithm is the first to employ spectral phase to quantify SWC morphology, making it possible to computationally distinguish SWC morphological subtypes and detect SWDs.

RESULTS/CONCLUSIONS: Proof-of-concept testing of the SWDfinder algorithm shows: (1) a major pattern of variation in SWC morphology maps to one axis of the phase difference distribution, (2) variability between the strain-specific distributions reflects differences in the proportions of SWC subtypes generated during SWD, and (3) regularities in the spectral power and phase profiles of SWCs can be used to detect waveforms possessing SWC-like morphology.

J Neurosci Methods 2015 Mar 15; 242:127-40.