Earticle

초록

영어

In this research, a novel method based on Canonical Correlation Analysis (CCA) and Artificial Neural Network (ANN) to detect epileptic seizures from EEG signals is proposed. CCA was applied on EEG signals and feature vectors corresponding to Eigen values were extracted. These Eigen values were fed as input to Artificial Neural Network (ANN)’s widely explored model Multilayer Perceptron Neural Networks (MLPNNs) for classification between occurrence of non-epileptic seizures and epileptic seizures. The extracted Eigen values using CCA proved to be a better epileptic seizures detector and provide average classification accuracy, sensitivity and specificity as 92.583%, 93.25% and 91% respectively.

목차

Abstract
 1. Introduction
 2. Material and Methods
  2.1. EEG Data Sets
  2.2. CCA Based Method for Epileptic Seizures Detection
  2.3. Classification
  2.4. Proposed Method
 3. Results and Discussion
 4. Conclusion
 References

키워드

저자

• Mumtaz Hussain Soomro [ Faculty of Engineering, Science and Technology (FEST), Indus University, Karachi. Pakistan. ]
• Sayed Hyder Abbas Musavi [ Faculty of Engineering, Science and Technology (FEST), Indus University, Karachi. Pakistan. ]
• Bishwajeet Pandey [ Chitkara University, Panjab, India. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605