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

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 and the 2nd, 3rd and 4th harmonics. The three phase differences were then used 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 subtypes and detect SWDs. Results/conclusions Proof-of-concept testing of the SWDreader 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 proportion 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.

An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

“…Our data show that events along the spectrum of the SVM score (and thus SWD confidence) show temporal and proportional correlations with abrupt changes in EEG power bands. 16 We also identified SWD-like events in wild-type mice, consistent with the hypothesis that SWD events represent a corruption of otherwise normal brain processes. Qualitatively similar temporal patterns were observed in all of the γ2R43Q animals analyzed in this study (data not shown).…”

“…The algorithm was trained using a dataset with 2500 putative events and labels (SWD or nonSWD) from 4 expert human scorers, using fivefold internal cross validation, and tested against the performance of 2 human scorers (S1 and S4) on multiple, unannotated (no computer information), and out-of-training data records. Although other groups have developed algorithms for the detection of SWDs, [16][17][18][19] to the best of our knowledge, no other group has designed an algorithm to mirror human confidence in scoring. Instead, it drew fuzzy, tightly interleaved boundaries similar to those of human scorers.…”

“…A, A hypnogram showing WAKE (blue), nREM (green), and REM (magenta) epochs (4 s) manually scored by R.M. 25 Continued development of this algorithm may also include components shown to be important for other SWD detection algorithms such as using phase information from the wavelet transform such as was done by Richard et al 16 In addition, some concern may exist about the nonstandard normalization procedure used in this algorithm. The vertical height of each bar represents the distance to nearest support vector.…”

“…Although algorithms have been developed for detection of SWDs, [16][17][18][19] there has been little development of algorithms for the detection or quantification of SWDs in mice. Although algorithms have been developed for detection of SWDs, [16][17][18][19] there has been little development of algorithms for the detection or quantification of SWDs in mice.…”

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

“…A SWD reader for mice was recently described (Richard et al, 2015). The cortical ECoG's(3 bipolar differential and 3 referenced recordings) of mice carrying the Gria4, Gabrg2, or Scn8a mutations showing SWDs were analysed with a Morlet wavelet transform and both spectral power and spectral phase values were calculated, the latter in order to quantify morphological variants of the SWDs, a feature that has not been quantified before.…”