ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges
This work addresses the challenge of precise epilepsy diagnosis from EEG data, which is crucial for patients and clinicians, by introducing a novel method that improves detection accuracy, though it is domain-specific and incremental in its approach.
The paper tackled patient-independent detection of epileptic activities from EEG signals by proposing ScatterFormer, a scattering transform-based Transformer that captures subtle high-frequency features, achieving median AUCROC of 98.14% and accuracy of 96.39% in Rolandic epilepsy and outperforming state-of-the-art by 9% in AUCROC on a neonatal seizure benchmark.
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.