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Advisor(s)
Abstract(s)
Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting
periodic activity that is composed of two alternate electroencephalogram patterns, which is considered
to be a marker of sleep instability. Experts usually score this pattern through a visual examination of
each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that
is prone to errors. To address these issues, a home monitoring device was developed for automatic
scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram
derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and
gated recurrent unit) and one one-dimension convolutional neural network, were developed and
tested to determine which was more suitable for the cyclic alternating pattern phase’s classification.
It was verified that the network based on the long short-term memory attained the best results
with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic
curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state
machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%,
71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’
expected agreement range and considerably higher than the inter-scorer agreement of multiple
experts, implying the usability of the device developed for clinical analysis.
Description
Keywords
Sleep quality EEG CAP GRU LSTM 1D-CNN . Faculdade de Ciências Exatas e da Engenharia
Citation
Mendonça, F., Mostafa, S. S., Morgado-Dias, F., & Ravelo-García, A. G. (2019). A portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivation. Entropy, 21(12), 1203.
Publisher
MDPI