A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction - Sanguk Ryu; Inwhee Joe (Applied Sciences, 2021)
The number of people diagnosed with epilepsy as a common brain disease accounts for about 1% of the world’s total population. Seizure prediction is an important study that can improve the lives of patients with epilepsy, and, in recent years, it has attracted more and more attention. In this paper, we propose a novel hybrid deep learning model that combines a Dense Convolutional Network (DenseNet) and Long Short-Term Memory (LSTM) for epileptic seizure prediction using EEG data. The proposed method first converts the EEG data into the time-frequency domain through Discrete Wavelet Transform (DWT) for use in the input of the model. Then, we train the previously transformed image through a hybrid model combining Densenet and LSTM. To evaluate the performance of the proposed method, experiments are conducted for each preictal length of 5, 10, and 15 min using the CHB-MIT scalp EEG dataset. As a result, we obtained a prediction accuracy of 93.28%, a sensitivity of 92.92%, a specificity of 93.65%, a false positive rate of 0.063 per hour, and an F1-score of 0.923 when the preictal length was 5 min. Finally, as the proposed method is compared to previous studies, it is confirmed that the seizure prediction performance was improved significantly.