Extracting Salient Features for EEG-based Diagnosis of Alzheimer's Disease Using Support Vector Machine Classifier

ABSTRACT

Alzheimer's disease (AD) is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for the diagnosis of AD and its progression. Prediction of cognitive performance of subjects from electroencephalography (EEG) and identification of relevant biomarkers are some of the research problems. Although EEG is a powerful and relatively cheap tool for the diagnosis of AD and dementia, it does not achieve the standards of clinical performance in terms of sensitivity and specificity to accept as a reliable technique for the screening of AD. Hence, there is an immense need to develop an efficient system and algorithms for diagnosis. Accordingly, the objective of this research paper is to analyze different features for the diagnosis of AD to serve as a possible biomarker to distinguish between AD subject and normal subject. The research is carried out on an experimental database. Three different features such as spectral-, wavelet-, and complexity-based features are computed and compared on the basis of classification accuracy obtained. The classification is carried out using support vector machine classifier giving 96% accuracy on complexity-based features and increased performance in terms of sensitivity and specificity. The results show the improved performance in the diagnosis of AD. It is observed that the severity of AD is depicted in EEG complexity. These features used in research work can be considered as the benchmark for AD diagnosis.

KEYWORDS:

• Alzheimer's disease
• Electroencephalography
• Preprocessing
• Feature extraction
• Support vector machine

ACKNOWLEDGMENTS

The authors acknowledge Smt. Kashibai Navale Medical College and General Hospital for providing necessary EEG database for carrying the research work. The authors would like to thank all the authors of the references as well as the reviewers for improving the quality of the paper.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Funding

Savitribai Phule Pune University [15ENG000865].

Notes on contributors

N. N. Kulkarni

N. N. Kulkarni has completed M.E. (electronics and telecommunication) from All India Shri Shivaji Memorial Society's Institute of Information Technology, Pune, in 2015. His areas of interests include biomedical signal and image processing, pattern recognition, and machine learning. He has published two papers in international and IEEE conference. He has filed one patent in technical field. Presently, he is working on biomedical signal processing applications. He is a member of IETE and IEI, India.

E-mail: [email protected]

V. K. Bairagi

Vinayak. K. Bairagi has completed M.E. (electronics) from Sinhgad COE, Pune, in 2007 (first rank in Pune University). The University of Pune has awarded him a PhD degree in engineering. He has 10 years of teaching experience and 7 years of research experience. He has filed eight patents and five copyrights in technical field. He has published more than 50 papers, of which 26 papers are in international journals of which 7 papers in SCI/Scopus Indexing list with four Springer journal publications along with the one in The IET journal publication. He is a reviewer for nine scientific journals. He is the P.I. for UoP-BCUD research grant. He has received IEI national level Young Engineer Award (2014) and ISTE national level Young Researcher Award (2015) for his excellence in the field of engineering. He is the member of INENG (UK), IETE (India), ISTE (India), and BMS (India). He is a recognized PhD guide in electronics engineering of Savitribai Phule Pune University.

E-mail: [email protected]