Automated detection of Alzheimerüs disease using EEG signal processing and machine learning

Mahbuba Ferdowsi; Haipeng Liu; Ban-Hoe Kwan; Choon-Hian Goh

doi:10.1201/9781003479970-8

Automated detection of Alzheimerüs disease using EEG signal processing and machine learning

Mahbuba Ferdowsi, Haipeng Liu, Ban-Hoe Kwan, Choon-Hian Goh

Centre for Intelligent Healthcare

Universiti Tunku Abdul Rahman

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Citations (Scopus)

22 Downloads (Pure)

Abstract

A parametric representation of Alzheimer’s disease (AD) indicates cognitive, behavioral, and intellectual difficulties. Aging is the biggest risk factor for most neurodegenerative diseases including AD. Age-related genetic, biochemical, structural, and functional changes commonly contribute to the liability to AD. Clinical electroencephalogram (EEG) technology enables the detection of the underlying neurophysiological abnormalities associated with AD, such as neuronal loss, synaptic dysfunction, and aberrant network connections, which can be reflected in the variations of EEG features and their temporal evolution. EEG data can be analyzed using a range of signal processing techniques, including discrete wavelet transform, the Burg method, and average periodogram, to investigate the cerebral activity linked to various cognitive and behavioral activities. Artificial intelligence (AI) can improve the efficiency of signal processing and achieve automatic classification of patients. Interest is growing in the use of AI algorithms and other cutting-edge computational techniques to analyze EEG data for the diagnosis of AD. By assisting in the detection of specific changes in EEG patterns, these techniques might enable earlier and more accurate diagnosis of AD.

Original language	English
Title of host publication	Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing
Editors	Rajesh Kumar Tripathy, Ram Bilas Pachori
Publisher	CRC Press, Taylor & Francis Group
Chapter	8
Pages	118-135
Number of pages	18
Edition	1
ISBN (Electronic)	9781003479970
ISBN (Print)	9781032529301
DOIs	https://doi.org/10.1201/9781003479970-8
Publication status	Published - 6 Jun 2024

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

Alzheimer's disease
EEG
Machine learning

Access to Document

10.1201/9781003479970-8

Liu2024AAMAccepted author manuscript, 692 KBLicence: CC BY-NC-ND

Cite this

Ferdowsi, M., Liu, H., Kwan, B.-H., & Goh, C.-H. (2024). Automated detection of Alzheimerüs disease using EEG signal processing and machine learning. In R. K. Tripathy, & R. B. Pachori (Eds.), Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing (1 ed., pp. 118-135). CRC Press, Taylor & Francis Group. https://doi.org/10.1201/9781003479970-8

Automated detection of Alzheimerüs disease using EEG signal processing and machine learning. / Ferdowsi, Mahbuba; Liu, Haipeng; Kwan, Ban-Hoe et al.
Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing. ed. / Rajesh Kumar Tripathy; Ram Bilas Pachori. 1. ed. CRC Press, Taylor & Francis Group, 2024. p. 118-135.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Automated detection of Alzheimerüs disease using EEG signal processing and machine learning

Abstract

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Keywords

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