Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy.
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 whi...
| Published in: | BioMed Research International Vol. 2015; pp. 1 - 15 |
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| Main Authors: | , , , , , , , , |
| Format: | research tables/charts tracings Journal Article |
| Published: |
Wiley-Blackwell
3/5/2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273584&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273584 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/5/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273584 109273584 109273584 10.1155/2015/638036 109273584 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy. aug: au: Ahmad, Malik Anas Ayaz, Yasar Jamil, Mohsin Omer Gillani, Syed Rasheed, Muhammad Babar Imran, Muhammad Khan, Nadeem Ahmed Majeed, Waqas Javaid, Nadeem affil: SMME, National University of Sciences & Technology, Islamabad 44000, Pakistan sug: subj: Epilepsy Diagnosis Electroencephalography Decision Support Systems, Clinical Computers and Computerization Human Funding Source Computer Simulation Data Analysis Software ab: 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. pubtype: Academic Journal doctype: research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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