# Wavelet-Based Multi-Class Seizure Type Classification System
## 1 Introduction  
### Background  
Epilepsy is the most widespread brain disease among children and adults after stroke [1]. It is defined as \"a sudden and recurrent brain malfunction and is a disease that reflects an excessive and hypersynchronous activity of the neurons within the brain\" [2]. Over 60 million of the world's population are diagnosed with epilepsy, whose defining feature is recurrent seizures. Such seizure attacks impair the brain's normal functions, leading the patient to be vulnerable and unsafe.  
Seizures are medically classified into two main categories--focal seizures or generalized seizures--depending on the extent to which regions of the brain are affected. Focal seizures are seizures that originate and affect a circumscribed region of the brain. Focal seizures are further classified into simple or complex, based on the patient's level of awareness. Generalized seizures, on the other hand, involve most areas of the brain. Based on motor and non-motor symptoms, generalized seizure classifications can be absence, tonic, atomic, clonic, tonic-clonic, or myoclonic seizures [3, 4]. Classification of seizure is very essential for accurate diagnosis and treatment.  
Identifying the type of seizure, although sometimes difficult, can be done by clinical observation and referencing medical history and demographic information, and is supported by general brain imaging techniques such as EEG, magnetoencephalography (MEG), and fMRI [5, 6]. EEG is the most practical and cost-effective tool to diagnose epilepsycurrently [7]. Video-EEG monitoring is often required to support the decision for seizure classification [8].  
For treatment, seizures can be controlled in most cases (up to 70%) of patients by consuming medication to achieve a steady-state concentration in the blood. Surgical intervention is another option for certain conditions. For up to 20% of epileptic patients, there is no medical treatment that exists to control seizures [2]. The accurate identification of the type of seizures influences medication choice and provides information to patients, families, researchers, and clinicians caring for patients with epilepsy [4; 9].  
It is a challenging task to classify the type of seizure accurately. Several factors make the classification difficult. Firstly, some types of seizures share the same clinical and EEG symptoms. For instance, it has been shown that even for a highly experienced neurologist, sometimes it is hard to distinguish between focal and generalized seizures [10]. Secondly, in some cases, it is required to perform long-term monitoring (i.e., video-EEG monitoring), which may last for days [7]. Therefore, manual analysis of these long recordings requires a substantial amount of effort and time from neurologists.  
In addition, signal interpretation is known to have a low inter-rater agreement, which fully depends on the level of expertise of the expert. Moreover, inter-subject variability significantly adds to the difficulties associated with the diagnosis of an epileptic seizure, leading to a variety of manifestations of the same type of seizures across different patients, and sometimes for the same individual over time. Finally, signal artifacts also hinder the correct interpretation of EEG. With these challenges, in a field that already has a shortage of healthcare experts, computer-aided diagnostic (CAD) methods have great potential to support decision making in the diagnosis of such a critical disease.