Sleep staging and analysis play a crucial role in understanding the mechanisms of sleep and diagnosing sleep disorders. Traditionally, sleep staging has required manual scoring of electroencephalography (EEG) data, which is labor-intensive and can be prone to errors.
Neuroinformatic methods, such as machine learning and deep learning, aim to automate and standardize the sleep staging process and have helped improve the accuracy and reliability of sleep staging, by providing objective measures of sleep parameters that were previously subjective and prone to inter-observer variability. These tools have revolutionized the field of sleep research and have enabled clinicians and researchers to gain deeper insights into the mechanisms of sleep and sleep-related disorders.
This Research Topic aims to explore innovative ways to stage the different phases of sleep using advanced technology and computational tools related to neuroinformatics.
The scope of this Research Topic includes but is not limited to
Development of open-source software tools for sleep staging and analysis, that utilize neuroinformatic methods
Evaluation of novel spectral measures that can accurately detect changes in sleep stages during overnight studies.
Exploration of deep learning models that can accurately predict sleep stage annotations and improve the accuracy of sleep-related diagnoses.
Investigation of the applicability and effectiveness of innovative neuroinformatic methods in improving the accuracy and efficiency of sleep staging, ultimately enhancing the diagnosis and treatment of sleep disorders.
Comparison of the performance of neuroinformatic methods with traditional manual sleep staging methods, including assessment of their inter-rater reliability and reproducibility.
Investigation of the limitations and challenges of neuroinformatic methods for sleep staging and analysis, including potential biases in machine learning models and the need for expert supervision and validation.
Overall, this Research Topic aims to advance the field of sleep staging and analysis through the incorporation of innovative neuroinformatic methods and to ultimately improve the accuracy and efficiency of sleep-related diagnoses and treatments.
Keywords: Sleep staging, Sleep electroencephalograms (EEG), Neuroinformatic methods, Machine Learning, Sleep disorders
Sleep staging and analysis play a crucial role in understanding the mechanisms of sleep and diagnosing sleep disorders. Traditionally, sleep staging has required manual scoring of electroencephalography (EEG) data, which is labor-intensive and can be prone to errors.
Neuroinformatic methods, such as machine learning and deep learning, aim to automate and standardize the sleep staging process and have helped improve the accuracy and reliability of sleep staging, by providing objective measures of sleep parameters that were previously subjective and prone to inter-observer variability. These tools have revolutionized the field of sleep research and have enabled clinicians and researchers to gain deeper insights into the mechanisms of sleep and sleep-related disorders.
This Research Topic aims to explore innovative ways to stage the different phases of sleep using advanced technology and computational tools related to neuroinformatics.
The scope of this Research Topic includes but is not limited to
Development of open-source software tools for sleep staging and analysis, that utilize neuroinformatic methods
Evaluation of novel spectral measures that can accurately detect changes in sleep stages during overnight studies.
Exploration of deep learning models that can accurately predict sleep stage annotations and improve the accuracy of sleep-related diagnoses.
Investigation of the applicability and effectiveness of innovative neuroinformatic methods in improving the accuracy and efficiency of sleep staging, ultimately enhancing the diagnosis and treatment of sleep disorders.
Comparison of the performance of neuroinformatic methods with traditional manual sleep staging methods, including assessment of their inter-rater reliability and reproducibility.
Investigation of the limitations and challenges of neuroinformatic methods for sleep staging and analysis, including potential biases in machine learning models and the need for expert supervision and validation.
Overall, this Research Topic aims to advance the field of sleep staging and analysis through the incorporation of innovative neuroinformatic methods and to ultimately improve the accuracy and efficiency of sleep-related diagnoses and treatments.
Keywords: Sleep staging, Sleep electroencephalograms (EEG), Neuroinformatic methods, Machine Learning, Sleep disorders
