Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG
Identifikátory výsledku
Kód výsledku v 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>
Nalezeny alternativní kódy
RIV/00216208:11150/21:10435240 RIV/00179906:_____/21:10435240 RIV/68407700:21220/21:00351487
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Medical & Biological Engineering & Computing
ISSN
0140-0118
e-ISSN
—
Svazek periodika
59
Číslo periodika v rámci svazku
11-12
Stát vydavatele periodika
DE - Spolková republika Německo
Počet stran výsledku
10
Strana od-do
2287-2296
Kód UT WoS článku
000696775600001
EID výsledku v databázi Scopus
2-s2.0-85115045182