Sleep Disorder Diagnosis Using EEG Signals and LSTM Deep Learning Method
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911130426802176 |
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| author | Yousefi, Mohammad Reza Rahimi, Reza |
| author_facet | Yousefi, Mohammad Reza Rahimi, Reza |
| contents | Diagnosing sleep disorders is an important focus in neuroscience and engineering, as these conditions involve issues such as insufficient sleep, frequent awakenings, and difficulty reaching deep sleep. Accurate detection based on brain signals, particularly electroencephalography (EEG), enables development of personalized treatments. While statistical pattern recognition was once the standard for analyzing EEG, deep learning has become the dominant approach. In this study, we analyzed a public database of 197 full night sleep recordings from participants aged 25-101 years. After preprocessing and feature extraction, Long Short-Term Memory (LSTM) neural networks achieved 93.3% accuracy in distinguishing healthy from disordered sleep, which improved to 95% with fusion techniques. The models were also computationally efficient, suggesting strong clinical potential for rapid and precise sleep disorder diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sleep Disorder Diagnosis Using EEG Signals and LSTM Deep Learning Method Yousefi, Mohammad Reza Rahimi, Reza Neurons and Cognition Quantitative Methods Diagnosing sleep disorders is an important focus in neuroscience and engineering, as these conditions involve issues such as insufficient sleep, frequent awakenings, and difficulty reaching deep sleep. Accurate detection based on brain signals, particularly electroencephalography (EEG), enables development of personalized treatments. While statistical pattern recognition was once the standard for analyzing EEG, deep learning has become the dominant approach. In this study, we analyzed a public database of 197 full night sleep recordings from participants aged 25-101 years. After preprocessing and feature extraction, Long Short-Term Memory (LSTM) neural networks achieved 93.3% accuracy in distinguishing healthy from disordered sleep, which improved to 95% with fusion techniques. The models were also computationally efficient, suggesting strong clinical potential for rapid and precise sleep disorder diagnosis. |
| title | Sleep Disorder Diagnosis Using EEG Signals and LSTM Deep Learning Method |
| topic | Neurons and Cognition Quantitative Methods |
| url | https://arxiv.org/abs/2509.00208 |