Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/71930
Title: A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation
Authors: Mendonca, Fabio
Mostafa, Sheikh Shanawaz
Morgado-Dias, Fernando
Ravelo-Garcia, Antonio G. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Automatic Method
Sleep
Cap
Classification
Sleep Quality, et al
Issue Date: 2019
Journal: Entropy 
Abstract: 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.
URI: http://hdl.handle.net/10553/71930
DOI: 10.3390/e21121203
Source: Entropy [ISSN 1099-4300], v. 21 (12), 1203, (Diciembre 2019)
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