… for seizure detection for the specific evaluation of seizure type classification. Then, we introduce
our framework based on memory networks … work introduced for anomaly detection [25] to …

… In terms of optimal design, seizure detection from EEG data … handcrafted feature extraction
for seizure detection. However, … Neural Networks along with long short-term memory. The …

… This study addresses these challenges using a deep neural network approach to seizure
detection using data from a research-grade wearable device. To address the limited availability …

… long short-term memory network and time-frequency analysis for the classification of epileptic
EEG signals. On contrary to other approaches, LSTM networks are capable of learning long…

… Neural Networks and Long Short-Term Memory networks have been used to analyze the
signal values using spectrogram visual analysis and sequence text-based detection for …

… Abstract—Automatic seizure detection plays a … seizure detection method based on Stockwell
transform (S-transform) and bidirectional long short-term memory (BiLSTM) neural networks …

… Short Term Memory (LSTM) network for classifying abnormality and normality of seizures. The
… Generally, the presented seizure detection model suffers from the issue of a better tradeoff …

… In this paper, we focus specifically on the problem of seizure detection. Many algorithms
have been applied to this problem including time–frequency analysis methods [5][6], nonlinear …

… Proper diagnosis and advance prediction of epileptic seizures definitely improves the life of
… seizure detection and prediction method using stacked bidirectional long short term memory …

… to-end epileptic seizure prediction approach based on the long short-term memory network
(… ) signals is extracted as network input directly for seizure prediction, thus avoiding subjective …