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L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets

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journal contribution
posted on 2023-07-26, 16:07 authored by Prabal Datta Barua, Ilknur Tuncer, Emrah Aydemir, Oliver Faust, Subrata Chakraborty, Vinithasree Subbhuraam, Turker Tuncer, Sengul Dogan, U Rajendra Acharya
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.

History

Refereed

  • Yes

Volume

12

Issue number

10

Publication title

Diagnostics

ISSN

2075-4418

Publisher

MDPI AG

File version

  • Published version

Language

  • eng

Legacy posted date

2023-02-01

Legacy creation date

2023-02-01

Legacy Faculty/School/Department

Faculty of Science & Engineering