Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG

Int J Environ Res Public Health. 2022 Sep 1;19(17):10892. doi: 10.3390/ijerph191710892.


The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (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.

Keywords: CAP A phase; Genetic algorithm; LSTM; Particle Swarm Optimization; information fusion.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Arousal
  • Electroencephalography* / methods
  • Humans
  • Polysomnography / methods
  • Sleep*
  • Time Factors

Grants and funding

This research was funded by LARSyS (Project - UIDB/50009/2020), and by ARDITI-Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação under the scope of the project M1420-09-5369-FSE-000002-Post-Doctoral Fellowship, co-financed by the Madeira 14-20 Program-European Social Fund. Diogo Freitas was supported by the Portuguese Foundation for Science and Technology with the grant number 2021.07966.BD. This research was also funded by Project MTL-Marítimo Training Lab, number M1420-01-0247-FEDER-000033.