Autoregessive Models in Alzheimer's Disease Classification from EEG
Result description
Linear predictive, back-predictive, and smoothing models were identified in the case of EEG signal. Signal quasi-stacionarity was quantified via robust statistical criteria, which is useful for Alzheimer's disease classification.
Keywords
Alzheimer's diseaseEEGlinear predictive modelquasi-stacionarityrobust statisticsFDR
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Autoregessive Models in Alzheimer's Disease Classification from EEG
Original language description
Linear predictive, back-predictive, and smoothing models were identified in the case of EEG signal. Signal quasi-stacionarity was quantified via robust statistical criteria, which is useful for Alzheimer's disease classification.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Doktorandské dny 2012
ISBN
978-80-01-05138-2
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
277-284
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Praha
Event date
Nov 16, 2012
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
CEP
JD - Use of computers, robotics and its application
Year of implementation
2012