Sleep Track: Automated Detection and Classification of Sleep Stages
Sleep is vital for our body’s physical restoration, but sleep disorders can cause various problems. Determining sleep stages is essential for diagnosing and curing such disorders. Polysomnography (PSG) signals are recordings of brain activity, eye movements, muscle activity and other physiological s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01020.pdf |
| _version_ | 1797658757130354688 |
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| author | Ram Kumar R.P. Rithesh A. Josh Pranav Karthik Raj B. John Vivek Shiva Prasad Doma |
| author_facet | Ram Kumar R.P. Rithesh A. Josh Pranav Karthik Raj B. John Vivek Shiva Prasad Doma |
| author_sort | Ram Kumar R.P. |
| collection | DOAJ |
| description | Sleep is vital for our body’s physical restoration, but sleep disorders can cause various problems. Determining sleep stages is essential for diagnosing and curing such disorders. Polysomnography (PSG) signals are recordings of brain activity, eye movements, muscle activity and other physiological signals that are collected during a sleep study. Insomnia, Sleep Apnea, and Restless Legs Syndrome are some of the sleep problems that can be identified using these signals. However, analysing PSG signals manually can be time-consuming and prone to errors. Deep Learning Models such as Convolutional Neural Networks (CNN), can be used to automate the analysis of PSG signals. CNN is followed by Long-Short Term Memory (LSTM) and CNN are used as a stack ensemble method to recognize patterns in the signals that correspond to different sleep stages and events. By training these models on large datasets of PSG signals, they can detect the disorders. The dataset is collected from PhysioNet Sleep-EDF dataset that consists of PSG signals. The accuracies obtained using different training and testing data using CNN and CNN-LSTM are 95.15% and 83.9% respectively, and using metadata classifier the overall accuracy is increased by 1%. The future enhancement of the paper can be done by considering Heart rate, EEG Pz-oz signals and EEG Pz-oz along with EEG Fpz-cz. |
| first_indexed | 2024-03-11T18:03:52Z |
| format | Article |
| id | doaj.art-24626491d26543f786ddf4b2031afc8f |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-11T18:03:52Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-24626491d26543f786ddf4b2031afc8f2023-10-17T08:47:10ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014300102010.1051/e3sconf/202343001020e3sconf_icmpc2023_01020Sleep Track: Automated Detection and Classification of Sleep StagesRam Kumar R.P.0Rithesh A.1Josh Pranav2Karthik Raj B.3John Vivek4Shiva Prasad Doma5Department of CSE (AIML), GRIETDepartment of CSE (AIML), GRIETDepartment of CSE (AIML), GRIETDepartment of CSE (AIML), GRIETUttaranchal Institute of Technology, Uttaranchal UniversityKG Reddy College of Engineering & TechnologySleep is vital for our body’s physical restoration, but sleep disorders can cause various problems. Determining sleep stages is essential for diagnosing and curing such disorders. Polysomnography (PSG) signals are recordings of brain activity, eye movements, muscle activity and other physiological signals that are collected during a sleep study. Insomnia, Sleep Apnea, and Restless Legs Syndrome are some of the sleep problems that can be identified using these signals. However, analysing PSG signals manually can be time-consuming and prone to errors. Deep Learning Models such as Convolutional Neural Networks (CNN), can be used to automate the analysis of PSG signals. CNN is followed by Long-Short Term Memory (LSTM) and CNN are used as a stack ensemble method to recognize patterns in the signals that correspond to different sleep stages and events. By training these models on large datasets of PSG signals, they can detect the disorders. The dataset is collected from PhysioNet Sleep-EDF dataset that consists of PSG signals. The accuracies obtained using different training and testing data using CNN and CNN-LSTM are 95.15% and 83.9% respectively, and using metadata classifier the overall accuracy is increased by 1%. The future enhancement of the paper can be done by considering Heart rate, EEG Pz-oz signals and EEG Pz-oz along with EEG Fpz-cz.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01020.pdf |
| spellingShingle | Ram Kumar R.P. Rithesh A. Josh Pranav Karthik Raj B. John Vivek Shiva Prasad Doma Sleep Track: Automated Detection and Classification of Sleep Stages E3S Web of Conferences |
| title | Sleep Track: Automated Detection and Classification of Sleep Stages |
| title_full | Sleep Track: Automated Detection and Classification of Sleep Stages |
| title_fullStr | Sleep Track: Automated Detection and Classification of Sleep Stages |
| title_full_unstemmed | Sleep Track: Automated Detection and Classification of Sleep Stages |
| title_short | Sleep Track: Automated Detection and Classification of Sleep Stages |
| title_sort | sleep track automated detection and classification of sleep stages |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01020.pdf |
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