Assessing serial dependence in ordinal patterns processes using chi-squared tests with application to EEG data analysis.
Chaos
; 32(7): 073126, 2022 Jul.
Article
en En
| MEDLINE
| ID: mdl-35907712
ABSTRACT
We extend Elsinger's work on chi-squared tests for independence using ordinal patterns and investigate the general class of m-dependent ordinal patterns processes, to which belong ordinal patterns processes derived from random walk, white noise, and moving average processes. We describe chi-squared asymptotically distributed statistics for such processes that take into account necessary constraints on ordinal patterns probabilities and propose a test for m-dependence, with which we are able to quantify the range of serial dependence in a process. We apply the test to epilepsy electroencephalography time series data and observe shorter m-dependence associated with seizures, suggesting that the range of serial dependence decreases during those events.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Epilepsia
/
Análisis de Datos
Límite:
Humans
Idioma:
En
Revista:
Chaos
Asunto de la revista:
CIENCIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
República Checa