Epilepsy is a neurological disorder that occurs due to abnormal activity in the brain. Symptoms can vary, such as uncontrolled movements, muscle stiffness, difficulty breathing, loss of consciousness, and even death. Therefore, the multichannel electroencephalogram (EEG) is very important to understand the pattern of seizure occurrence and non-seizure in epilepsy. In this paper, we determine an automatic epilepsy detection method using enhanced Salp Swarm Algorithm (SSA) CNN-based of EEG signals. The signal is transformed into Low Pass Filter (LPF) and High Pass Filter (HPF) with one level, frequencies, and scales using Wavelet Transform. Enhanced SSA was used to determine the number of neurons and the appropriate number of convolution layers in the CNN algorithm for classifying two classes (epilepsy and epilepsy with seizure) using the CHB-MIT dataset from Children's Hospital Boston. The results of the study show that the proposed method produces the highest accuracy of 99.15% and 89.04% of average accuracy. This result is obtained with a computation time on testing data of 0.0001 seconds using a high-end computer. Enhanced SSA was proven to increase the performance of CNN of 81.13%. The proposed method can be used in the automatic detection of epilepsy.
|Number of pages||9|
|Journal||Journal of Theoretical and Applied Information Technology|
|Publication status||Published - 15 Oct 2022|
- Convolutional Neural Network
- Salp Swarm Algorithm
- Wavelet Transform