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Seizure onset detection with minimum latency has a key role in improving the therapy studies of epilepsy. In this article, an epileptic seizure onset detection algorithm based on general tensor discriminant analysis is proposed to detect the seizure through EEG signals with smallest delay before the development of clinical symptoms. In this algorithm, seizure and nonseizure EEG signal epochs are exhibited by spectral, spatial, and temporal domains (third-order tensors) in wavelet decomposition. Then, to reduce feature space, projection matrices are extracted from tensor-represented EEG signal by general tensor discriminant analysis. In this strategy, the discriminative information in the training tensors is preserved that it is a benefit in comparison with common feature space reduction algorithms such as principal component analysis and multilinear subspace analysis. The proposed seizure onset detection algorithm is evaluated on 44 epileptic patients from 2 standard datasets and recognizes 98% of seizures with average delay of 4.5 seconds. The obtained results show efficiency and effectiveness of our proposed algorithm in comparison with other algorithms.

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