Barua, Prabal Datta 
ORCID: https://orcid.org/0000-0001-5117-8333, Tuncer, Ilknur, Aydemir, Emrah ORCID: https://orcid.org/0000-0002-8380-7891, Faust, Oliver ORCID: https://orcid.org/0000-0002-3979-4077, Chakraborty, Subrata ORCID: https://orcid.org/0000-0002-0102-5424, Subbhuraam, Vinithasree, Tuncer, Turker ORCID: https://orcid.org/0000-0002-5126-6445, Dogan, Sengul ORCID: https://orcid.org/0000-0001-9677-5684 and Acharya, U Rajendra ORCID: https://orcid.org/0000-0003-2689-8552
(2022)
L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets.
Diagnostics, 12 (10).
ISSN 2075-4418
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Abstract
<jats:p>Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.</jats:p>
| Item Type: | Journal Article |
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| Keywords: | Science & Technology, Life Sciences & Biomedicine, Medicine, General & Internal, General & Internal Medicine, L-tetrolet pattern, sleep stage expert system, multiple pooling decomposition, insomnia, EEG signal classification, RESEARCH RESOURCE, NEURAL-NETWORK, CHANNEL, IDENTIFICATION, ENSEMBLE, SIGNALS, SYSTEM, FEATURES, CAP |
| Faculty: | Faculty of Science & Engineering |
| SWORD Depositor: | Symplectic User |
| Depositing User: | Symplectic User |
| Date Deposited: | 01 Feb 2023 16:36 |
| Last Modified: | 01 Feb 2023 16:36 |
| URI: | https://arro.anglia.ac.uk/id/eprint/708222 |
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