Epileptic seizure classification using fuzzy lattices and Neural Reinforcement Learning

ABSTRACT

This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques.

KEYWORDS:

• EEG
• Neural Reinforcement Learning
• seizure
• fuzzy lattices
• Schrödinger equation

Disclosure statement

No potential conflict of interest was reported by the author(s)

Author statement

Amit Kukker, Rajneesh Sharma, Om Mishra, Deepak Parashar certify that they have seen and approved the final version of the manuscript being submitted. They warrant that the article is their original work, hasn’t received prior publication and isn’t under consideration for publication elsewhere.

Additional information

Notes on contributors

Amit Kukker

Amit Kukker obtained his B.Tech in Electrical Engineering from JECRC, Jaipur, and M.Tech in ICE from SLIET University, Punjab. He obtained his Ph.D. in ICE from Netaji Subhas Institute of Technology, University of Delhi, Delhi. He is currently working with Chandigarh University, Punjab.

Rajneesh Sharma

Rajneesh Sharma obtained his B.E. in Electrical Engineering from DCE, Delhi University, and M.E. in Control & Instrumentation from DCE, Delhi University. He obtained his Ph.D. in Intelligent Control of Non-Linear Systems from IIT, Delhi. After that, Rajneesh carried out Post-Doctoral research at Institute for Systems and Robotics, IST, Lisbon, Portugal, for one year. He has published more than 70 research papers in reputed referreedjournals and conferences. Currently, he is Professor with the Division of ICE of NSUT, Delhi.

Om Mishra

Om Mishra obtained his Ph.D. from DTU,Delhi, India. He is currently working with Symbiosis University, Pune, India.

Deepak Parashar

Deepak Parashar obtained his Ph.D. from MANIT, Bhopal, India. He is currently working with Symbiosis University, Pune, India.