# Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

## Abstract

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%.

Authors
Fábio Mendonça; Sheikh Shanawaz Mostafa; Diogo Freitas; Fernando Morgado-Dias; Antonio G. Ravelo-García
Date
2022
Journal
Entropy
Publisher
MDPI
Source

## Citation

@article{mendonca2022,
author = {Mendonça, Fábio and Mostafa, Sheikh Shanawaz and Freitas, Diogo and Morgado-Dias, Fernando and Ravelo-García, Antonio G.},
doi = {10.3390/e24050688},
journal = {Entropy},
month = {5},
number = {5},
pages = {688},
title = {Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection},
volume = {24},
year = {2022}
}