Feature Fusion for Epileptic Seizure Detection from EEG Data: A CNN- Based Approach with High Precision

This study presents a novel method for automatic seizure identification in epilepsy by applying a deep convolutional neural network (CNN) to spectrogram pictures of electroencephalogram (EEG) data. The research uses the Python programming language and Jupyter Notebook for its implementation; its goal is to improve seizure detection throughput and precision. By converting EEG data to spectrogram pictures, the approach provides a rich time frequency representation ideal for enhanced feature extraction. In addition, a random forest (RF) method is used for auxiliary feature extraction, which boosts the model’s discriminatory ability. The deep CNN undergoes training using spectrogram images, facilitating the autonomous classification of EEG signals into two distinct categories: one indicative of a healthy state and the other representing ictal conditions. The implementation is accomplished using Python and the powerful TensorFlow and Keras libraries within a Jupyter Notebook environment. To test the consistency and applicability of the proposed method, the experimental assessment uses the Bonn University database, which contains a various situation. The results show that the deep CNN model is highly effective at differentiating between normal EEG patterns and those caused by seizures, with a remarkable classification accuracy of 98.4%. Furthermore, this work adds to the increasing body of knowledge in automated seizure identification, which may have implications for real-time monitoring and enhanced patient care beyond the high classification accuracy observed.