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Publication
Sleep Analysis by Evaluating the Cyclic Alternating Pattern A Phases
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Advisor(s)
Abstract(s)
Sleep is a complex process divided into different stages, and a decrease in sleep quality can
lead to adverse health-related effects. Therefore, diagnosing and treating sleep-related conditions
is crucial. The Cyclic Alternating Pattern (CAP) is an indicator of sleep instability and can assist in
assessing sleep-related disorders such as sleep apnea. However, manually detecting CAP-related
events is time-consuming and challenging. Therefore, automatic detection is needed. Despite their
usually higher performance, the utilization of deep learning solutions may result in models that lack
interpretability. Addressing this issue can be achieved through the implementation of feature-based
analysis. Nevertheless, it becomes necessary to identify which features can better highlight the
patterns associated with CAP. Such is the purpose of this work, where 98 features were computed
from the patient’s electroencephalographic signals and used to train a neural network to identify
the CAP activation phases. Feature selection and model tuning with a genetic algorithm were also
employed to improve the classification results. The proposed method’s performance was found to be
among the best state-of-the-art works that use more complex models.
Description
Keywords
CAP A phase EEG Feature interpretation Model optimization Sleep analysis . Faculdade de Ciências Exatas e da Engenharia
Citation
Alves, A.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. Sleep Analysis by Evaluating the Cyclic Alternating Pattern A Phases. Electronics 2024, 13, 333. https:// doi.org/10.3390/electronics13020333
Publisher
MDPI