AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders
Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used to automatically classify sleep stages with high computing spe...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2023-09-01
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| Series: | Frontiers in Biomedical Technologies |
| Subjects: | |
| Online Access: | https://fbt.tums.ac.ir/index.php/fbt/article/view/486 |
| _version_ | 1797658885602934784 |
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| author | Mahtab Vaezi Mehdi Nasri |
| author_facet | Mahtab Vaezi Mehdi Nasri |
| author_sort | Mahtab Vaezi |
| collection | DOAJ |
| description |
Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used to automatically classify sleep stages with high computing speed, which is robust to noise. SAEs is a kind of neural networks with two encoder and decoder blocks, and ten hidden layers in each block. The function of these networks is similar to the human brain, and is capable of automatically processing signals. To prove the efficiency of this network, in addition to examining the effect of various biological signals such as ECG and EEG on the performance of sleep stage classification, SHHS and ISRUC standard databases have been used. The accuracy of classifying 2 to 6 classes by SHHS database are 1.00, 0.993, 0.9880, 0.9688, 0.961, and on ISRUC database accuracies are 1.00, 1.00, 0.996, 0.9431. Moreover, the proposed network can classify wake, deep sleep, and light sleep using the ECG signal (acc=0.75, kappa=0.69). In the review of the results, it is concluded that sleep stages classification based on EEG signal have better results, still acquisition of ECG signal, and its acceptable results can be a good alternative to use. In addition to its high ability of the proposed method to detect sleep stages, this network is robust to noise, which is very necessary and important for the clinical processing of sleep signals.
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| first_indexed | 2024-03-11T18:06:51Z |
| format | Article |
| id | doaj.art-1a68f1bef41a42d8a54550fba4186b22 |
| institution | Directory Open Access Journal |
| issn | 2345-5837 |
| language | English |
| last_indexed | 2024-03-11T18:06:51Z |
| publishDate | 2023-09-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Biomedical Technologies |
| spelling | doaj.art-1a68f1bef41a42d8a54550fba4186b222023-10-17T05:06:42ZengTehran University of Medical SciencesFrontiers in Biomedical Technologies2345-58372023-09-0110410.18502/fbt.v10i4.13722AS3-SAE: Automatic Sleep Stages Scoring using Stacked AutoencodersMahtab Vaezi0Mehdi Nasri10000-0001-9049-5681Islamic Azad University, Khomeinishar branch Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used to automatically classify sleep stages with high computing speed, which is robust to noise. SAEs is a kind of neural networks with two encoder and decoder blocks, and ten hidden layers in each block. The function of these networks is similar to the human brain, and is capable of automatically processing signals. To prove the efficiency of this network, in addition to examining the effect of various biological signals such as ECG and EEG on the performance of sleep stage classification, SHHS and ISRUC standard databases have been used. The accuracy of classifying 2 to 6 classes by SHHS database are 1.00, 0.993, 0.9880, 0.9688, 0.961, and on ISRUC database accuracies are 1.00, 1.00, 0.996, 0.9431. Moreover, the proposed network can classify wake, deep sleep, and light sleep using the ECG signal (acc=0.75, kappa=0.69). In the review of the results, it is concluded that sleep stages classification based on EEG signal have better results, still acquisition of ECG signal, and its acceptable results can be a good alternative to use. In addition to its high ability of the proposed method to detect sleep stages, this network is robust to noise, which is very necessary and important for the clinical processing of sleep signals. https://fbt.tums.ac.ir/index.php/fbt/article/view/486Sleep stagesStacked AutoencoderSingle channel EEGDeep learningECG. |
| spellingShingle | Mahtab Vaezi Mehdi Nasri AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders Frontiers in Biomedical Technologies Sleep stages Stacked Autoencoder Single channel EEG Deep learning ECG. |
| title | AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders |
| title_full | AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders |
| title_fullStr | AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders |
| title_full_unstemmed | AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders |
| title_short | AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders |
| title_sort | as3 sae automatic sleep stages scoring using stacked autoencoders |
| topic | Sleep stages Stacked Autoencoder Single channel EEG Deep learning ECG. |
| url | https://fbt.tums.ac.ir/index.php/fbt/article/view/486 |
| work_keys_str_mv | AT mahtabvaezi as3saeautomaticsleepstagesscoringusingstackedautoencoders AT mehdinasri as3saeautomaticsleepstagesscoringusingstackedautoencoders |