Classification and automatic scoring of arousal intensity during sleep stages using machine learning
Abstract Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to mea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50653-9 |
| _version_ | 1797259336325529600 |
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| author | Hyewon Han Min Jae Seong Janghun Hyeon Eunyeon Joo Junhyoung Oh |
| author_facet | Hyewon Han Min Jae Seong Janghun Hyeon Eunyeon Joo Junhyoung Oh |
| author_sort | Hyewon Han |
| collection | DOAJ |
| description | Abstract Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability. Therefore, this study aims to identify a highly accurate classifier for each sleep stage by employing optimized feature selection and machine learning models. According to electroencephalography (EEG) signals during the arousal phase, the intensity level was categorized into four levels. For control, the non-arousal cases were used as level 0 and referred as sham arousal, resulting in five arousal intensity levels. Wavelet transform was applied to analyze sleep arousal to extract features from EEG. Based on these features, we classified arousal intensity levels through machine learning algorithms. Due to the different characteristics of EEG in each sleep stage, the classification model was optimized for the four sleep stages. Excluding sham arousals, a total of 13,532 arousal events were used. The lowest intensity in the entire data, level 1, was computed to be 3107, level 2 was 3384, level 3 was 3472, and the highest intensity of level 4 was 3,569. The optimized classification model for each sleep stage achieved an average sensitivity of 82.68%, specificity of 95.68%, and AUROC of 96.30%. The sensitivity of the control, arousal intensity level 0, was 83.07%, a 1.25% increase over the unoptimized model and a 14.22% increase over previous research. This study used machine learning techniques to develop classifiers for each sleep stage, improving the accuracy of arousal intensity classification. The classifiers showed high sensitivity and specificity and revealed the unique characteristics of arousal intensity during different sleep stages. These findings represent a novel approach to arousal research and have implications for developing more accurate predictive models in sleep research. |
| first_indexed | 2024-04-24T23:07:48Z |
| format | Article |
| id | doaj.art-293d6c806fa6452b90c6ad41c815df56 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T23:07:48Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-293d6c806fa6452b90c6ad41c815df562024-03-17T12:22:40ZengNature PortfolioScientific Reports2045-23222024-03-0114111210.1038/s41598-023-50653-9Classification and automatic scoring of arousal intensity during sleep stages using machine learningHyewon Han0Min Jae Seong1Janghun Hyeon2Eunyeon Joo3Junhyoung Oh4Department of Computer Engineering, Hongik UniversityDepartment of Neurology, Bundang Jesaeng HospitalDepartment of Artificial Intelligence Software, Hanbat National UniversityDepartment of Neurology, Neuroscience center, Samsung Medical Center, Sungkyunkwan University School of MedicineDivision of Information Security, Seoul Women’s UniversityAbstract Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability. Therefore, this study aims to identify a highly accurate classifier for each sleep stage by employing optimized feature selection and machine learning models. According to electroencephalography (EEG) signals during the arousal phase, the intensity level was categorized into four levels. For control, the non-arousal cases were used as level 0 and referred as sham arousal, resulting in five arousal intensity levels. Wavelet transform was applied to analyze sleep arousal to extract features from EEG. Based on these features, we classified arousal intensity levels through machine learning algorithms. Due to the different characteristics of EEG in each sleep stage, the classification model was optimized for the four sleep stages. Excluding sham arousals, a total of 13,532 arousal events were used. The lowest intensity in the entire data, level 1, was computed to be 3107, level 2 was 3384, level 3 was 3472, and the highest intensity of level 4 was 3,569. The optimized classification model for each sleep stage achieved an average sensitivity of 82.68%, specificity of 95.68%, and AUROC of 96.30%. The sensitivity of the control, arousal intensity level 0, was 83.07%, a 1.25% increase over the unoptimized model and a 14.22% increase over previous research. This study used machine learning techniques to develop classifiers for each sleep stage, improving the accuracy of arousal intensity classification. The classifiers showed high sensitivity and specificity and revealed the unique characteristics of arousal intensity during different sleep stages. These findings represent a novel approach to arousal research and have implications for developing more accurate predictive models in sleep research.https://doi.org/10.1038/s41598-023-50653-9 |
| spellingShingle | Hyewon Han Min Jae Seong Janghun Hyeon Eunyeon Joo Junhyoung Oh Classification and automatic scoring of arousal intensity during sleep stages using machine learning Scientific Reports |
| title | Classification and automatic scoring of arousal intensity during sleep stages using machine learning |
| title_full | Classification and automatic scoring of arousal intensity during sleep stages using machine learning |
| title_fullStr | Classification and automatic scoring of arousal intensity during sleep stages using machine learning |
| title_full_unstemmed | Classification and automatic scoring of arousal intensity during sleep stages using machine learning |
| title_short | Classification and automatic scoring of arousal intensity during sleep stages using machine learning |
| title_sort | classification and automatic scoring of arousal intensity during sleep stages using machine learning |
| url | https://doi.org/10.1038/s41598-023-50653-9 |
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