Earticle

초록

영어

Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence physical injury may occur. Proper health care must be provided to the patients and this can be achieved only if the seizures are detected correctly in time. In this dissertation work, a system is designed using wavelet decomposition method and different training algorithms to train the neural network for classification of the EEG signals. The system was tested and compared with Support Vector Machine (SVM) classifier. The system accuracy comes out to be 99.97%.

목차

Abstract
 1. Introduction
  1.1 EEG Signals
  1.2 International 10-20 System
  1.3 Wavelet Transform
  1.4 Energy Distribution
  1.5 Artificial Neural Networks
  1.6 Support Vector Machine
 2. Methodology
 3. Methods
  3.3 Participants
  3.4 Apparatus
 4. Results and Discussion
  4.3 Results with Different Training Algorithms
 5. Conclusion
 References

키워드

저자

• Prabhpreet Kaur Bhatia [ CTITR, Jalandhar ]
• Anurag Sharma [ CTITR, Jalandhar ]

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