Inception networks, data augmentation and transfer learning in EEG-based photosensitivity diagnosis

Abstract Photosensitivity refers to a neurophysiological condition in which the brain generates epileptic discharges known as Photoparoxysmal Responses (PPR) in response to light flashes. In severe cases, these PPR can lead to epileptic seizures. The standardized diagnostic procedure for this condition is called Intermittent Photic Stimulation. During this procedure, the patient is exposed to a flashing light, aiming to trigger these epileptic reactions while preventing their full development. Meanwhile, brain activity is monitored using Electroencephalography, which is visually analyzed by clinical staff to identify these responses. Hence, the automatic detection of PPR becomes a highly unbalanced problem that has been barely studied in the literature due to photosensitivity’s low prevalence. This research tackles this problem and proposes using Inception-based deep learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. A data augmentation (DA) technique is also applied to balance the available data set, evaluating its effects on the DL models. The proposal outperformed state-of-the-art solutions in the literature, achieving higher ratios on standard performance metrics, and with DA significantly improving the Sensitivity without affecting Accuracy and Specificity. This project is currently being developed with patients from Burgos University Hospital, Spain.