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Syst."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p> Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person\u2019s life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time\u2013frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. 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