  Patient-independent detection of epileptic activities based on visual
spectral representation of continuous EEG (cEEG) has been widely used for
diagnosing epilepsy. However, precise detection remains a considerable
challenge due to subtle variabilities across subjects, channels and time
points. Thus, capturing fine-grained, discriminative features of EEG patterns,
which is associated with high-frequency textural information, is yet to be
resolved. In this work, we propose Scattering Transformer (ScatterFormer), an
invariant scattering transform-based hierarchical Transformer that specifically
pays attention to subtle features. In particular, the disentangled
frequency-aware attention (FAA) enables the Transformer to capture clinically
informative high-frequency components, offering a novel clinical explainability
based on visual encoding of multichannel EEG signals. Evaluations on two
distinct tasks of epileptiform detection demonstrate the effectiveness our
method. Our proposed model achieves median AUCROC and accuracy of 98.14%,
96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection
benchmark, it outperforms the state-of-the-art by 9% in terms of average
AUCROC.
