A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal

Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy diagnosis. Scholars have suggested automated techniques to detect the ailment due to the lengthy process and global shortage of specialists. Most research in the past years has been conducted utilizing machine learning methods. But following the development of deep learning methods, many groups are employing it to make computer-aided diagnostic (CAD) systems. In this work, the authors have proposed a model comprising of Convolutional Neural Network (CNN) and long short-term memory (LSTM) to detect seizures. It focuses on extracting temporal and spatial features by integrating CNN and LSTM models. The advantage of this automated system is it extracts spatial as well as temporal features from EEG signals with less trainable parameters and gives good accuracy. This makes the system suile for real-time processing applications. It has achieved a maximum of 100% accuracy, 100% sensitivity, and 100% specificity to distinguish between healthy and seizure patients.