Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/74387
Title: Cyclic alternating pattern estimation based on a probabilistic model over an EEG signal
Authors: Mendonça, Fábio
Mostafa, Sheikh Shanawaz
Morgado-Dias, Fernando
Ravelo-García, Antonio G. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: CAP
GMM
HMM
Sleep Analysis
SOM
Issue Date: 2020
Journal: Biomedical Signal Processing and Control 
Abstract: A probabilistic model for sleep analysis is proposed in this work, modeling the temporal relation between the sleep structure and the presence of the electroencephalogram (EEG) Cyclic Alternating Pattern (CAP) with a Hidden Markov Model (HMM). Sleep scoring is frequently performed by assigning a state to each thirty second epoch. However, this approach does not provide enough time resolution to efficiently detect the CAP since, by definition, the CAP cycles are assessed by applying the scoring rules to one second epochs of the EEG signal. Thus, a clustering analysis was employed, with a one second epoch, over the EEG signal to create clusters that were then encoded using symbolic dynamics to produce words. Two algorithms for clustering were analyzed, specifically the self-organizing map and the Gaussian Mixture Model (GMM). The words were then fed to the HMM to determine the presence of the CAP. Both single-channel and multi-channel (based on sensor fusion) approaches were tested. The best results were attained using the GMM with three Gaussians, achieving an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 72%, 66%, 75% and 0.71 for single-channel and 76%, 61%, 85% and 0.73 for multi-channel. This results are in the specialist agreement range with visual analysis. Therefore, the proposed model is capable of providing a new view over the CAP cycles by simplifying the complex EEG signal to a simple sequence of symbols. Such analysis can be significantly challenging to perform in more abstract models.
URI: http://hdl.handle.net/10553/74387
ISSN: 1746-8094
DOI: 10.1016/j.bspc.2020.102063
Source: Biomedical Signal Processing and Control [ISSN 1746-8094], v. 62, 102063, (Septiembre 2020)
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