Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network
Current methods for sleep stage detection rely on sensors to collect physiological data. These methods are inaccurate and take up considerable medical resources. Thus, in this study, we propose a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model to automatical...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10442 |
| _version_ | 1797581365794832384 |
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| author | Chun-Jung Lin Cheng-Jian Lin Xue-Qian Lin |
| author_facet | Chun-Jung Lin Cheng-Jian Lin Xue-Qian Lin |
| author_sort | Chun-Jung Lin |
| collection | DOAJ |
| description | Current methods for sleep stage detection rely on sensors to collect physiological data. These methods are inaccurate and take up considerable medical resources. Thus, in this study, we propose a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model to automatically detect and classify sleep stages. In the proposed T-MCCFNN model, multiscale convolution kernels extract features of the input electroencephalogram signal and a compensatory fuzzy neural network is used in place of a traditional fully connected network as a classifier to improve the convergence rate during learning and to reduce the number of model parameters required. Due to the complexity of general deep learning networks, trial and error methods are often used to determine their parameters. However, this method is very time-consuming. Therefore, this study uses the Taguchi method instead, where the optimal parameter combination is identified over a minimal number of experiments. We use the Sleep-EDF database to evaluate the proposed model. The results indicate that the proposed T-MCCFNN sleep stage classification accuracy is 85.3%, which is superior to methods proposed by other scholars. |
| first_indexed | 2024-03-10T23:04:18Z |
| format | Article |
| id | doaj.art-490a01ceddcb4c948b7738c8f14cd095 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:18Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-490a01ceddcb4c948b7738c8f14cd0952023-11-19T09:27:48ZengMDPI AGApplied Sciences2076-34172023-09-0113181044210.3390/app131810442Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural NetworkChun-Jung Lin0Cheng-Jian Lin1Xue-Qian Lin2Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanCurrent methods for sleep stage detection rely on sensors to collect physiological data. These methods are inaccurate and take up considerable medical resources. Thus, in this study, we propose a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model to automatically detect and classify sleep stages. In the proposed T-MCCFNN model, multiscale convolution kernels extract features of the input electroencephalogram signal and a compensatory fuzzy neural network is used in place of a traditional fully connected network as a classifier to improve the convergence rate during learning and to reduce the number of model parameters required. Due to the complexity of general deep learning networks, trial and error methods are often used to determine their parameters. However, this method is very time-consuming. Therefore, this study uses the Taguchi method instead, where the optimal parameter combination is identified over a minimal number of experiments. We use the Sleep-EDF database to evaluate the proposed model. The results indicate that the proposed T-MCCFNN sleep stage classification accuracy is 85.3%, which is superior to methods proposed by other scholars.https://www.mdpi.com/2076-3417/13/18/10442sleep stage classificationdeep learning networkfuzzy neural network (FNN)Taguchi methodelectroencephalography |
| spellingShingle | Chun-Jung Lin Cheng-Jian Lin Xue-Qian Lin Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network Applied Sciences sleep stage classification deep learning network fuzzy neural network (FNN) Taguchi method electroencephalography |
| title | Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network |
| title_full | Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network |
| title_fullStr | Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network |
| title_full_unstemmed | Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network |
| title_short | Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network |
| title_sort | automatic sleep stage classification using a taguchi based multiscale convolutional compensatory fuzzy neural network |
| topic | sleep stage classification deep learning network fuzzy neural network (FNN) Taguchi method electroencephalography |
| url | https://www.mdpi.com/2076-3417/13/18/10442 |
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