Epilepsy classification using optimized artificial neural network

ABSTRACT

Objectives: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process.

Materials: In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point.

Results: For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification.

Discussion: Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction.

Abbreviations: EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.

KEYWORDS:

• Epilepsy
• ictal
• inter-ictal
• electroencephalogram
• computer aided diagnosis
• mean square error

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Jagriti Saini

Jagriti Saini received Bachelor’s degree from Himachal Pradesh University, India in 2013 and Master’s degree (Electronics and Communication Engineering) from NITTTR, Panjab University, India in 2017. She received Gold Medal for her Master’s degree from Panjab University, India. At present, she is a Ph.D. candidate in the Electronics and Communication Engineering Department at NITTTR, Chandigarh. Her research interests lie in Neural Networks, Image Processing, Artificial Intelligence and IoT. She has published one extensive review paper in a reputed SCI indexed journal.

Maitreyee Dutta

Maitreyee Dutta is working as a Professor in the Computer Science and Engineering Department at NITTTR, Chandigarh, India. She has a Ph.D. (Engineering and Technology) with specialization in Image Processing and M.E. in Electronics Communication and Engineering from Panjab University, Chandigarh and a B.E. in Electronics Communication and Engineering Guwahati University, India. She has over seventeen years of teaching experience. Her research interests include digital signal processing, advanced computer architecture, data warehousing and mining, image processing. She has more than 90 research publications in reputed journals and conferences.