Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform.

ABSTRACT: Excessive electrical activity in the brain causes epileptic seizures which can be detected through Electroencephalogram (EEG) signals. The research aims to identify epileptic seizures using EEG records automatically. Firstly, EEG bands are extracted using Discrete Wavelet Transform (DWT) and concatenated. Secondly, the resulting feature vector is fed into a 1-dimensional Convolutional Neural Network (CNN) to extract spatial information. The Long-Short Term Memory (LSTM) layer then receives the feature maps in order to extract the temporal information. Ultimately, a fully connected layer will use the generated spatiotemporal features as input to categorize the signal. Results show that the suggested model performs well on the following datasets: the TUSZ corpus, which has 94.32% accuracy, 8

SUBMITTER: Kashefi Amiri H 

PROVIDER: S-EPMC12464174 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature