استفاده از IMFs نرمال شده برای تشخیص تشنج صرعی در سیگنالهای EEG
Abstract
1- Introduction
2- Methodology
3- Results and discussion
4- Conclusions
References
Abstract
Epilepsy is the fourth most common neurological disorder that manifests itself through unprovoked seizures, detection of which is the very first step of proper diagnosis and treatment of this severe disease. In this paper, an automated seizure detection method has been proposed based on the statistical and spectral features of max normalized intrinsic mode functions or IMFs that were extracted using complete ensemble empirical mode decomposition with adaptive noise method. First, a publicly available dataset of EEG signals was used to generate the IMFs and noise or outliers were discarded. Then IMFs were max normalized which was shown to improve the separability of features. Statistical and spectral features were extracted from the normalized IMFs which offered better separation of seizure and seizure-free data. Finally, Quadratic Discriminant classifier was used for the classification purpose and 10-fold cross validation was performed to validate the trained model. The proposed scheme is numerically efficient and shows a maximum of 100% accuracy which is the highest reported on this data set.
Motivation
The International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) have defined the terms epileptic seizure and epilepsy as follows: an epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain and epilepsy is a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures and by the neurobiologic, cognitive, psychological, and social consequences of this condition [1]. The definition of epilepsy requires the occurrence of at least one epileptic seizure. According to World Health Organization (WHO), approximately 50 million people have epilepsy worldwide. Around 80% of epilepsy patients are either from low or middleincome countries, of which around 75% failto get proper treatment. Epilepsy has severe socioeconomic consequences, including high treatment cost,low employment rate, andlow salary among epileptic patients [2]. Epilepsy remains resistant to drug therapy in about one-third of patients and people with pharmacoresistant epilepsy are about 2–10 times more likely to die compared to the general population [3].