Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/134588
Título: Heuristic Optimization ofDeep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection
Autores/as: Mendonça, Fábio
Mostafa, Sheikh Shanawaz
Freitas, Diogo
Morgado-Dias, Fernando
Ravelo García, Antonio Gabriel
Clasificación UNESCO: 3311 tecnología de la instrumentación
Palabras clave: 1D-CNN
ANN
CAP
HOSA
LSTM
Fecha de publicación: 2022
Publicación seriada: Entropy 
Resumen: Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.
URI: http://hdl.handle.net/10553/134588
ISSN: 1099-4300
DOI: 10.3390/e24050688
Fuente: Entropy [ISSN 1099-4300], v. 24 (5), 688, (Mayo 2022)
Colección:Artículos
Vista completa
Adobe PDF (2,39 MB)

Citas SCOPUSTM   

2
actualizado el 15-dic-2024

Citas de WEB OF SCIENCETM
Citations

2
actualizado el 15-dic-2024

Google ScholarTM

Verifica


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.