# Online EEG Seizure Detection and Localization
## 1 Introduction  
More than 50 million people around the world struggle with epilepsy, with 5 million people diagnosed each year. In the U.S. there are more than 3 million people afflicted with the disease, with 180,000 new cases each year. The severity of epileptic seizures can vary from laughter to sudden unexpected death in epilepsy (SUDEP). The attacks can last from seconds to minutes [1; 2; 3]. While there is much that is unknown about epileptic seizures, the molecular mechanism involves an imbalance in the inhibition and excitation of neurons leading to hypersynchronization of neuronal activity [4; 5; 6; 7]. Epilepsy is a condition with recurrent seizures, and it can start at any age. Early detection not only increases life expectancy but can also prevent further damage during physiological development [8]. During childhood and adolescence (ages 4-21), the brain's gray matter density is decreasing because of synaptic pruning and increasing myelination [9]. Also during these ages, subcortical regions, such as the caudate, putamen, thalamus, amygdala, hippocampus, and the cerebellar cortex, are developing to form a mature brain [10]. Untreated seizures can cause severe biological, sociological, and psychological issues during these critical periods [11; 12]. A high ratio of epileptic patients in developing countries and poor patients in developed countries remain untreated. According to the World Health Organization (WHO), about three-quarters of the people with epilepsy in low-income countries may not receive proper treatment [13]. For instance, 9 out of 10 epilepsy patients remain untreated in Africa [14]. A major reason for this lack of treatment is the need for trained medical personnel for the detection and identification of seizures and for monitoring the progress of a patient. Unfortunately, the demand for neurologists outpaces their supply. In the U.S., studies estimate that the demand for neurologists will rise to almost 21,500 by 2025, while the number of neurologists will only increase to about 18,000 [15]. This problem can be at least partially alleviated with automated online seizure detection. This is especially true for long-term electroencephalographicmonitoring. Real-time seizure detection approaches can be used in epilepsy monitoring units (EMUs) to assist neurologists [16]. Outside of hospital settings, real-time seizure detection can be used to activate drug delivery systems [17] to inform caregivers of a potentially dangerous situation [18], or for neurostimulation [19]. Most epileptic seizure detection algorithms are either patient-specific or seizure-specific. In patient-specific algorithms, the assumption is that the patient has already been diagnosed with epilepsy, and previous seizure datasets are available for training [20; 21]. There are various types of clinical seizures, and seizure-specific algorithms are trained to detect particular seizure types [22]. Even though these algorithms can differentiate between seizure and non-seizure periods, the algorithms have to be trained extensively in order to obtain acceptable classification performance because much of the training is done without reference to the underlying physiological process. While such algorithms definitely have their advantages and uses in practice, there is also a need for algorithms that can detect seizures without requiring data on the patient's history in order to be trained. While there is much that remains to be understood about epileptic seizures, we know that there are certain characteristics common to most seizures. During a seizure, there is increased and synchronized activity in the brain known as hypersynchronization. Detecting both the increase in activity and the presence of hypersynchronization can provide an effective way of detecting the presence of an epileptic seizure.