Epileptic Seizures Detection Based on Empirical Mode Decomposition of EEG Signals

Epilepsy is a chronic neurological disorder that affects more than 50 million people world wide, characterized by recurrent seizures (World Health Organization [WHO], 2006). An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain (Fisher et al., 2005 & Berg et al., 2010). This electrical hyperactivity can have its source in different parts of the brain and produces physical symptoms such as short periods of inattention and loose of memory, a sensory hallucination, or a whole-body convulsion. The frequency of these events can vary from one in a year to several in a day. The majority of the patients suffer from unpredictable, persistent and frequent seizures which limit the independence of an individual, increase the risk of serious injury and mobility, and result in both social isolation and economic hardship (Friedman & Gilliam, 2010). In addition, the patients with epilepsy have an increased mortality risk of approximately 2 to 3 times that of the general population (Ficker, 2000). The first line of treatment for epilepsy is with multiple anti-epileptic drugs and it is effective in about 70% of the cases. From the 30% remaining affected individuals only less than 10% could benefit from surgical therapy leaving a 20% of the total of people with epilepsy who will continue suffering sudden, incontrollable seizures and for whom other forms of treatment are being investigated (Theodore & Ficker, 2004; WHO, 2006). For any of the reasons exposed before the seizure detection is an important component in the diagnosis of epilepsy and for the seizures control. In the clinical practice this detection basically involves visual scanning of Electroencephalogram (EEG) long recordings by the physicians in order to detect and classify the seizure activity present in the EEG signal. Usually these are multichannel records of 24 to 72 hours length which implies a very time consuming task and it is also kwon that the conclusions are very subjective so disagreement between physicians are not rare. The seek here is to detect automatically in long term EEG records those segments of the signal that present epileptic seizures for the shake of reducing the high amount of information to be analyzed by the neurologists. Thus them could focus their attention in these part of the information so a more precisely and quick diagnosis can be made. Seizure detection is also a useful tool for treatments such us timely drug delivery, electrical stimulation and seizure alert systems. Automated seizure detection, quantification and recognition have been of interest of the biomedical community researchers since the 1970s. In some initial works a number of