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A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements

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Title: A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements
Authors: Melissant, Co, Ypma, Alexander, Frietman, Edward E E, Stam, Cornelis J
Source: Melissant, C, Ypma, A, Frietman, E E E & Stam, C J 2005, 'A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements', Artificial Intelligence in Medicine, vol. 33, no. 3, pp. 209-222. https://doi.org/10.1016/j.artmed.2004.07.003
Publisher Information: Elsevier BV, 2005.
Publication Year: 2005
Subject Terms: Alzheimer Disease/diagnosis, Automated, Male, Time Factors, 02 engineering and technology, Pattern Recognition, Theta Rhythm/classification, Pattern Recognition, Automated, 03 medical and health sciences, Computer-Assisted, 0302 clinical medicine, Alzheimer Disease, 0202 electrical engineering, electronic engineering, information engineering, Humans, Theta Rhythm, Memory Disorders, Electroencephalography, Signal Processing, Computer-Assisted, Neural Networks (Computer), Middle Aged, 3. Good health, Memory Disorders/diagnosis, Signal Processing, Female, Neural Networks, Computer, Electroencephalography/classification, Artifacts, Algorithms
Description: Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease.The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively).Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage.We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.
Document Type: Article
Language: English
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2004.07.003
Access URL: https://pubmed.ncbi.nlm.nih.gov/15811786
https://dl.acm.org/citation.cfm?id=2232780
http://www.tnw.tudelft.nl/fileadmin/Faculteit/TNW/Over_de_faculteit/Afdelingen/Imaging_Science_and_Technology/Research/Research_Groups/Quantitative_Imaging/Publications/List_Publications/doc/Melissant.pdf
http://www.ncbi.nlm.nih.gov/pubmed/15811786
https://pubmed.ncbi.nlm.nih.gov/15811786/
https://europepmc.org/abstract/MED/15811786
https://dblp.uni-trier.de/db/journals/artmed/artmed33.html#MelissantYFS05
https://research.vumc.nl/en/publications/377c4d6b-744e-42ee-891b-0c6426b8aabb
https://hdl.handle.net/1871/26657
Rights: Elsevier TDM
Accession Number: edsair.doi.dedup.....96f083f1d37f4fa06f22682649ece690
Database: OpenAIRE
Description
Abstract:Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease.The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively).Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage.We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.
ISSN:09333657
DOI:10.1016/j.artmed.2004.07.003