Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F21%3A43904240" target="_blank" >RIV/60076658:12310/21:43904240 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11150/21:10435240 RIV/00179906:_____/21:10435240 RIV/68407700:21220/21:00351487
Result on the web
<a href="https://link.springer.com/article/10.1007/s11517-021-02427-6" target="_blank" >https://link.springer.com/article/10.1007/s11517-021-02427-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11517-021-02427-6" target="_blank" >10.1007/s11517-021-02427-6</a>

Result language
angličtina
Original language name
Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG
Original language description
Alzheimer's disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer's disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer's disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer's disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer's disease from EEG records.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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