A hybrid 1D-CNN with multi-head attention and wavelet preprocessing reportedly reaches 99.83% accuracy on the UCI epilepsy dataset, but the result is undermined by a leaky data split and internally inconsistent metrics.
Neurocomputing 414, 90–100 (2020)
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Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism
A hybrid 1D-CNN with multi-head attention and wavelet preprocessing reportedly reaches 99.83% accuracy on the UCI epilepsy dataset, but the result is undermined by a leaky data split and internally inconsistent metrics.