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EEG Signal Analysis for Mild Alzheimer’s Disease Diagnosis by Means of Spectral- and Complexity-Based Features and Machine Learning Techniques

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Proceedings of the 2nd International Conference on Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 828))

Abstract

Alzheimer’s disease (AD) is one of the common and fastest growing neurological diseases in the modern society. Biomarker techniques for diagnosis of Alzheimer’s disease and its progression in early stage are key issues for development. Electroencephalogram is one of the powerful techniques which can be used for screening of Alzheimer’s disease and dementia in early stage. The objective of this paper is to analyze the EEG signal by means of spectral and complexity features to serve EEG as a biomarker for Alzheimer’s diagnosis. The research is carried on experimental database obtained from hospital. EEG relative power, spectral entropy, spectral flux, and spectral centroid are analyzed, compared, and classified for separating the data between two groups by means of support vector machine (SVM) classifier and K-nearest neighbor (KNN) classifier. The obtained results indicate severity observed in AD patients reflected in EEG signals which can be treated as benchmark for Alzheimer’s diagnosis.

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Ethical Statement

The database in present research work was collected from Smt. Kashibai Navale Medical College and General Hospital, Pune, under the supervision of Dr. Nilima Bhalerao, Neurosurgeon. The EEG data set and its details were approved by ethical committee of the hospital and participants.

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Authors and Affiliations

  1. STES’s Sou. Venutai Chavan Polytechnic, Pune, India

    Nilesh Kulkarni

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  1. Nilesh Kulkarni
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Correspondence to Nilesh Kulkarni .

Editor information

Editors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Tai Kang

  4. Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Husseinzadeh Kashan

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Kulkarni, N. (2019). EEG Signal Analysis for Mild Alzheimer’s Disease Diagnosis by Means of Spectral- and Complexity-Based Features and Machine Learning Techniques. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_40

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  • DOI: https://doi.org/10.1007/978-981-13-1610-4_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1609-8

  • Online ISBN: 978-981-13-1610-4

  • eBook Packages: EngineeringEngineering (R0)

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