Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/107482
Título: On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes
Autores/as: Mendonca, Fabio
Mostafa, Sheikh Shanawaz
Morgado-Dias, Fernando
Ravelo-Garcia, Antonio G. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: A Phase Subtypes
CAP
LSTM
Recurrence Quantification Analysis
Sleep Quality
Fecha de publicación: 2021
Publicación seriada: Journal of Neural Engineering 
Resumen: The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods). It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.
URI: http://hdl.handle.net/10553/107482
ISSN: 1741-2560
DOI: 10.1088/1741-2552/abd047
Fuente: Journal Of Neural Engineering [ISSN 1741-2560], v. 18 (3), 036004, (Junio 2021)
Colección:Artículos
Unknown (1,8 MB)
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.