  We present a sparse and invariant representation with low asymptotic
complexity for robust unsupervised transient and onset zone detection in noisy
environments. This unsupervised approach is based on wavelet transforms and
leverages the scattering network from Mallat et al. by deriving frequency
invariance. This frequency invariance is a key concept to enforce robust
representations of transients in presence of possible frequency shifts and
perturbations occurring in the original signal. Implementation details as well
as complexity analysis are provided in addition of the theoretical framework
and the invariance properties. In this work, our primary application consists
of predicting the onset of seizure in epileptic patients from subdural
recordings as well as detecting inter-ictal spikes.
