Seizure Detection with 2D Spectrogram Using CNN and SVM Integration

Complex neurological disorders such as epilepsy are often diagnosed by the detailed analysis of electroencephalogram (EEG) signals. In this work, we investigate seizure classification using the 2DCNN-SVM model on EEG data. Our comprehensive evaluation, centered on EEG signals from the Bonn dataset, incorporates essential preprocessing steps like baseline correction and Short-Time Fourier Transform (STFT) analysis. EEG data can be thoroughly examined and analyzed in both the frequency and time domains via the STFT. It generates a representation based on spectrograms that have significant time-frequency details. This representation becomes the foundation for effective feature extraction using a 2D Convolutional Neural Network (2D CNN), while seizure detection is orchestrated through Support Vector Machines (SVM). Our model demonstrates its accuracy and versatility by successfully handling two-class (A vs E, B vs E, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</tex> vs E, D against E) and four-class (ABCD Vs E) classification problems. After a comprehensive comparative performance analysis, the model's performance in terms of accuracy and sensitivity shows the ability to identify seizures within EEG signals. This study contributes to epilepsy diagnosis and EEG-based seizure detection systems.