Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data

Authors

Abhijit Dasgupta, Indian Statistical Institute, Kolkata
Losiana Nayak, Indian Statistical Institute, Kolkata
Ritankar Das, Maulana Abul Kalam Azad University of Technology
Debasis Basu, Medical College and Hospital Kolkata
Preetam Chandra, Medical College and Hospital Kolkata
Rajat K. De, Indian Statistical Institute, Kolkata

Document Type

Conference Article

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

In this paper, we create EEG data derived signatures for differentiating epileptic patients from normal individuals. Epilepsy is a neurological condition of human beings, mostly treated based on a patient’s seizure symptoms. Clinicians face immense difficulty in detecting epileptic patients. Here we define brain region-connection based signatures from EEG data with help of various machine learning techniques. These signatures will help the clinicians in detecting epileptic patients in general. Moreover, we define separate signatures by taking into account a few demographic features like gender and age. Such signatures may aid the clinicians along with the generalized epileptic signature in case of complex decisions.

First Page

87

Last Page

95

DOI

10.1007/978-3-319-69900-4_11

Publication Date

1-1-2017

Recommended Citation

Dasgupta, Abhijit; Nayak, Losiana; Das, Ritankar; Basu, Debasis; Chandra, Preetam; and De, Rajat K., "Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data" (2017). Conference Articles. 297.
https://digitalcommons.isical.ac.in/conf-articles/297