Detecting Epileptic Seizures Using Symplectic Geometry Decomposition-Based Features and Gaussian Deep Boltzmann Machines

Studies deem that about 1 percent of the human population is affected by epileptic seizures on a global scale. It is characterized as an undue neuronal discharge in the brain and degrades the quality of life of the patients to a large extent. Children being unaware of a sudden onset of seizures could be affected by severe injury or even mortality. Machine-learning-based epileptic seizure detection from EEG (Electro-Encephalogram) signals have always been a hot area of research. However, the majority of the research works rely on correlated non-linear features extracted from the EEG signals, causing a high-computational overhead, and challenging their application in real-time clinical diagnosis. This study proposes a robust seizure detection framework using Gaussian Deep Boltzmann Machine-based classifier and Symplectic Geometric Decomposition (SGD)-based features. The simplified eigenvalues derived through Symplectic Similarity Transform (SST) are employed as feature vectors for the classifier, eliminating the need for a deliberate feature extraction procedure. The study examines the transferability capability of the suggested framework in discriminating seizures in both neonates and pediatric subjects in unison, experimenting with classical annotated datasets. The model yielded a mean accuracy of about 97.91% and an F1 Score of 0.935 in pediatric seizure detection, and mean sensitivity and specificity of 99.05% and 98.28%, in neonatal seizure detection tasks, respectively. Thus, the model can be deemed comparable to the available state-of-the-art seizure detection frameworks.