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Open AccessFeature PaperArticle

A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation

1
Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
2
Madeira Interactive Technologies Institute (ITI/Larsys/M-ITI), 9020-105 Funchal, Portugal
3
Faculty of Exact Sciences and Engineering, University of Madeira, 9000-082 Funchal, Portugal
4
Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1203; https://doi.org/10.3390/e21121203
Received: 16 October 2019 / Revised: 3 December 2019 / Accepted: 5 December 2019 / Published: 7 December 2019
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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. View Full-Text
Keywords: sleep quality; EEG; CAP; GRU; LSTM; 1D-CNN sleep quality; EEG; CAP; GRU; LSTM; 1D-CNN
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Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F.; Ravelo-García, A.G. A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation. Entropy 2019, 21, 1203.

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