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- Title
Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy.
- Authors
Ahmad, Malik Anas; Ayaz, Yasar; Jamil, Mohsin; Omer Gillani, Syed; Rasheed, Muhammad Babar; Imran, Muhammad; Khan, Nadeem Ahmed; Majeed, Waqas; Javaid, Nadeem
- Abstract
Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system’s incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.
- Subjects
DIAGNOSIS of epilepsy; COMPUTER simulation; COMPUTERS; DECISION support systems; ELECTROENCEPHALOGRAPHY; MEDICAL databases; INFORMATION storage &; retrieval systems; RESEARCH funding; DATA analysis software
- Publication
BioMed Research International, 2015, Vol 2015, p1
- ISSN
2314-6133
- Publication type
Article
- DOI
10.1155/2015/638036