Next Article in Journal
Modeling Expected Shortfall Using Tail Entropy
Next Article in Special Issue
Dynamic Analysis and Intelligent Control Strategy for the Internal Thermal Control Fluid Loop of Scientific Experimental Racks in Space Stations
Previous Article in Journal
Nonasymptotic Upper Bounds on Binary Single Deletion Codes via Mixed Integer Linear Programming
Previous Article in Special Issue
Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients
Open AccessFeature PaperArticle

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

Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
Madeira Interactive Technologies Institute (ITI/Larsys/M-ITI), 9020-105 Funchal, Portugal
Faculty of Exact Sciences and Engineering, University of Madeira, 9000-082 Funchal, Portugal
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;
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
Show Figures

Figure 1

MDPI and ACS Style

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.

AMA Style

Mendonça F, Mostafa SS, Morgado-Dias F, Ravelo-García AG. A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation. Entropy. 2019; 21(12):1203.

Chicago/Turabian Style

Mendonça, Fábio; Mostafa, Sheikh S.; Morgado-Dias, Fernando; Ravelo-García, Antonio G. 2019. "A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation" Entropy 21, no. 12: 1203.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop