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

作     者：Richard, C. D. Tanenbaum, A. Audit, B. Arneodo, A. Khalil, A. Frankel, W. N. 

作者机构：Jackson Lab Bar Harbor ME 04609 USA Univ Maine Grad Sch Biomed Sci & Engn Orono ME 04469 USA Washington Univ Sch Med Dept Neurol St Louis MO 63130 USA Univ Maine Dept Math C0mpuMAINE Lab Orono ME 04469 USA Univ Lyon Ecole Normale Super Lyon CNRS Lab PhysUMR 5672 F-69007 Lyon France Tufts Univ Sch Med Sackler Sch Boston MA 02111 USA 

出 版 物：《JOURNAL OF NEUROSCIENCE METHODS》 (神经科学方法杂志)

年 卷 期：2015年第242卷第0期

页      面：127-140页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 10[医学] 

基　　金：Maine Economic Improvement Fund (MEIF) fellowship NIH [NS031348] 

主　　题：Spike-wave discharge Spike-wave complex Phase differences Seizure detection algorithm Harmonic analysis Fundamental frequency Morphology Morlet wavelet transform Mouse mutant Absence epilepsy 

摘      要：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 degrees x 360 degrees x 360 degrees} 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 SWD finder 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. (C) 2014 Elsevier B.V. All rights reserved.