Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals

Epilepsy is one of the major neurological disorders that affects more than 50 million people around the world; it is characterized by unpredictable seizures due to an abnormal electrical activity in the brain. In this thesis nocturnal epilepsy has been investigated. In particular, Nocturnal Frontal Lobe Epilepsy (NFLE), that is a form of epilepsy in which seizures occur predominantly during sleep with symptoms including nocturnal awakenings, dystonic and tonic postures and clonic limb convulsions. The electroencephalographic (EEG) signals, which record the electrical activity of the brain, are used by neurologists to diagnose epilepsy. However, in almost 50% of NFLE cases, the EEG does not show abnormality during seizures, making the neurologists work to identify the epileptic events very difficult, thereby requiring the support of video recording to verify the epileptic events, with a subsequent time-consuming procedure. In literature few scientific contributions address the classification of nocturnal epileptic seizures. In this thesis, the automatic systems, both customized for single patient and generalized have been developed to find the best nocturnal epileptic seizure detection system from EEG signals. The combination of feature extraction and selection methods, associated to classification models based on Self Organizing Map (SOM), have been investigated following the classical machine learning approach. The ability of SOM to represent data from a high-dimensional space in a low-dimensional space, preserving the topological properties of the original space, has been exploited to identify nocturnal epileptic seizures and track the temporal projection of the EEG signals on the map. The proposed methods allow the definition of maps capable of presenting meaningful information on the actual brain state, revealing the mapping potential of clustering data coming from seizure and non-seizure states. The results obtained show that the patient-specific system achieves better performance than a patient-independent system. Moreover, comparing the performances with those of a binary classifier, widely used in epileptic seizure detection problems, the Support Vector Machine (SVM), the SOM model achieves good and, for some patients, higher performances. In particular, the patient-customized system using SOM model, reaches an average value of sensitivity and specificity equal to 82.85% and 89.92%, respectively; whereas the SVM classifier achieved an average sensitivity and specificity equal to 82.11% and 82.85%, respectively, suggesting the use of SOM model as a good alternative for nocturnal epileptic seizure detection. The discriminating power of SOM and the possibility to follow the temporal sequence of the EEG recordings on the map can provide information on an imminent epileptic seizure, highlighting the possibility to promote therapies aimed at rapid and targeted disarming the seizures.