title: Seizure classification in EEG signals utilizing Hilbert-Huang transform.
abstract: BACKGROUND
  Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.
  
  
  METHODS
  Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.
  
  
  RESULTS
  The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.
  
  
  CONCLUSIONS
  An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.
authors: Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan. oweis@just.edu.jo
authors: Rami J
authors: RJ
authors: Oweis
authors: Enas W
authors: EW
authors: Abdulhay
references: Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1650-3
references: 21096140
references: J Neurosci Methods. 2010 Jan 30;186(1):97-106
references: 19891985
references: Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1711-5
references: 19163009
references: Nat Med. 1998 Oct;4(10):1173-6
references: 9771751
references: Comput Intell Neurosci. 2007;:80510
references: 18301712
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Dec;64(6 Pt 1):061907
references: 11736210
references: Lancet. 2001 Jan 20;357(9251):183-8
references: 11213095
references: Electroencephalogr Clin Neurophysiol. 1992 Jan;82(1):30-7
references: 1370141
references: IEEE Trans Biomed Eng. 1997 Feb;44(2):115-22
references: 9214791
references: Comput Methods Programs Biomed. 2006 Mar;81(3):279-84
references: 16488052