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Publication
Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG
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Advisor(s)
Abstract(s)
The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalo gram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible
clinical applications; however, there is a need to develop automatic methodologies to facilitate
real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels’
feature level fusion was proposed in this work and employed for the CAP A phase classification.
Two optimization algorithms optimized the channel selection, fusion, and classification procedures.
The developed methodologies were evaluated by fusing the information from multiple EEG channels
for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results
showed that both optimization algorithms selected a comparable structure with similar feature level
fusion, consisting of three electroencephalogram channels (Fp2–F4, C4–A1, F4–C4), which is in line
with the CAP protocol to ensure multiple channels’ arousals for CAP detection. Moreover, the
two optimized models reached an area under the receiver operating characteristic curve of 0.82, with
average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement
and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has
the advantage of providing a fully automatic analysis without requiring any manual procedure.
Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being
thus suitable for real-world application.
Description
Keywords
CAP A phase Genetic algorithm Information fusion Particle Swarm optimization LSTM . Escola Superior de Tecnologias e Gestão Faculdade de Ciências Exatas e da Engenharia
Citation
Mendonça, F.; Mostafa, S.S.; Freitas, D.; Morgado-Dias, F.; Ravelo-García, A.G. Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG. Int. J. Environ. Res. Public Health 2022, 19, 10892. https://doi.org/10.3390/ ijerph191710892
Publisher
MDPI