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Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection
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Orientador(es)
Resumo(s)
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%.
Descrição
Palavras-chave
1D-CNN ANN CAP HOSA LSTM . Escola Superior de Tecnologias e Gestão Faculdade de Ciências Exatas e da Engenharia
Contexto Educativo
Citação
Mendonça, F.; Mostafa, S.S.; Freitas, D.; Dias, F. M.; Ravelo-García, A.G. Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection. Entropy 2022, 24, 688. https:// doi.org/10.3390/e24050688
Editora
MDPI