Machine learning algorithms for diagnosis of epilepsy from EEG - Thangavel, Prasanth (Nanyang Technological University, 2022)
Epilepsy is a set of chronic neurological brain diseases characterized by recurrent seizures. The Interictal Epileptiform Discharges (IEDs) play an important role in epilepsy diagnosis and management. According to the International Federation of Societies for Electroencephalography and Clinical Neurophysiology (IFSECN), IEDs are described as transients distinguishable from background activity, with a characteristic spiky shape, commonly, but not completely or universally, observed in interictal EEG recordings of epileptic people. Epilepsy affects about 70 million people worldwide. Epilepsy diagnosis based on IEDs in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify epileptic EEGs with IEDs vs. normal EEGs. There are only a few studies that have performed epileptic classification on EEG level, which has more clinical relevance than simply detecting IEDs. It is indeed important to consider the EEG holistically, as neurologists do, to come up with a final verdict regarding the entire EEG recording. Most studies in the literature consider a single dataset, while it is crucial to verify whether machine learning systems perform well on data from various centers; it is becoming clear in the AI community that machine learning systems tend to perform well on the datasets on which they have been trained, but may not always generalize robustly to data from other centers, seriously jeopardizing the practicality of machine learning in the medical domain. In this thesis, we developed a cascade of deep learning and shallow classifiers for reliable diagnosis of epilepsy based on IED-dependent and IED-independent features.