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 |
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