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- W4382279551 abstract "Epilepsy is a well-known neurological disease caused by malfunctioning nerve activity in the brain. These malfunctioning causes episodes called seizures. Seizures in epileptic patients involve uncontrollable movements, loss of sensation, convulsions, and loss of consciousness, which can result in catastrophic injury and even death. Therefore, a computerized seizure recognition system is important to protect epilepsy patients from the risk of seizures. The main reason for this disorder is still unknown. Though the symptoms associated with seizures can be treated manually and the accuracy of the diagnosis depends on the experience of the technician. In this paper, we presented an artificial intelligence-based approach where time–frequency characteristics of EEG signals are used to detect an epileptic seizure. Electroencephalography (EEG) is widely recognized for the diagnosis and evaluation of brain activity and disorders. We preprocessed the EEG signal and converted it into time–frequency information, then passed it through ANN-LSTM architecture for training. The proposed model achieves 99.46% classification accuracy with higher overall sensitivity (99.78%) and specificity (99.13%) and outperformed other similar methods. The findings of the study indicate that our technique has the potential for clinical application. The effectiveness of this strategy may be further assessed by combining it with other epileptic datasets." @default.
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- W4382279551 date "2023-01-01" @default.
- W4382279551 modified "2023-09-27" @default.
- W4382279551 title "Epileptic Seizure Detection from EEG Signal Using ANN-LSTM Model" @default.
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- W4382279551 doi "https://doi.org/10.1007/978-981-99-1916-1_10" @default.
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