Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

AM Tăuţan, AC Rossi, R de Francisco… - Biomedical Engineering …, 2021 - degruyter.com
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …
被引用次数：14 相关文章

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

AM Tăuţan, AC Rossi, R de Francisco, B Ionescu - Biomed. Eng, 2021 - degruyter.com
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis.

AM Tăuţan, AC Rossi, R de Francisco… - Biomedizinische …, 2020 - europepmc.org
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

AM Tăuţan, AC Rossi… - Biomedizinische …, 2020 - pubmed.ncbi.nlm.nih.gov
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis.

AM Tăuţan, AC Rossi… - Biomedical …, 2021 - search.ebscohost.com
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

AM Tăuţan, AC Rossi, R de Francisco, B Ionescu - 2020 - hero.epa.gov
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …