BiVISTA: A Bi-View Spectro-Temporal Conv-Transformer for Automated Annotation of Harmful Brain Activity

This is the authors' abstract. We don't add key points for this paper.

Continuous ICU EEG review is resource-intensive and limited by inter-rater variability. Clinicians interpret harmful patterns by jointly inspecting raw waveforms and region-wise spectrograms within a bipolar montage, integrating temporal context across multiple scales. Existing automated approaches, however, seldom encode these clinical priors or address the full six-class American Clinical Neurophysiology Society (ACNS) taxonomy. This study introduces BiVISTA , a clinically grounded Bi-View Spectro-Temporal Conv-Transformer. The methodology proceeds in four stages: (1) A Bi-View Super EEG Image is constructed by aligning multi-scale waveforms (10, 20, 50 s) with montage-preserving regional spectrograms, strictly enforcing anatomical topology. (2) A convolutional encoder performs manifold compression, extracting local spectro-temporal motifs into a compact embedding. (3) A six-layer Transformer encoder applies multi-head feature projections to refine and disentangle the compact representation. (4) A linear classifier outputs ACNS probabilities. Evaluated on the large-scale HBAC dataset, BiVISTA achieves a mean test accuracy of 96.0% ( \(±\) 0.3), macro-F1 of 0.960, and macro-AUROC of 99.6% across ten independent runs. Seizure sensitivity and specificity reach 95.0% and 99.0%, with a Cohen's \(κ\) of 0.952. Interpretability analyses demonstrate reliance on physiologically valid cues like focal lateralization, supporting trustworthy clinical decision-making.