FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2017689092> ?p ?o ?g. }

• W2017689092 endingPage "105" @default.
• W2017689092 startingPage "94" @default.
• W2017689092 abstract "Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts’ scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class “one-against-all” SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection." @default.
• W2017689092 created "2016-06-24" @default.
• W2017689092 creator A5003921571 @default.
• W2017689092 creator A5018557661 @default.
• W2017689092 creator A5023836510 @default.
• W2017689092 creator A5050565600 @default.
• W2017689092 creator A5051870567 @default.
• W2017689092 creator A5064646045 @default.
• W2017689092 creator A5069491921 @default.
• W2017689092 creator A5073040185 @default.
• W2017689092 date "2015-07-01" @default.
• W2017689092 modified "2023-10-17" @default.
• W2017689092 title "Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines" @default.
• W2017689092 cites W128541477 @default.
• W2017689092 cites W130969773 @default.
• W2017689092 cites W1528072437 @default.
• W2017689092 cites W1963938004 @default.
• W2017689092 cites W1967502714 @default.
• W2017689092 cites W1969529002 @default.
• W2017689092 cites W1972615077 @default.
• W2017689092 cites W1974498657 @default.
• W2017689092 cites W1976318001 @default.
• W2017689092 cites W197723069 @default.
• W2017689092 cites W1978448418 @default.
• W2017689092 cites W1982754133 @default.
• W2017689092 cites W1983313287 @default.
• W2017689092 cites W1983504371 @default.
• W2017689092 cites W1983998322 @default.
• W2017689092 cites W1991923537 @default.
• W2017689092 cites W1993578171 @default.
• W2017689092 cites W1999470896 @default.
• W2017689092 cites W2000837205 @default.
• W2017689092 cites W2004454719 @default.
• W2017689092 cites W2005509407 @default.
• W2017689092 cites W2012060192 @default.
• W2017689092 cites W2012533813 @default.
• W2017689092 cites W2014683958 @default.
• W2017689092 cites W2018048250 @default.
• W2017689092 cites W2019642871 @default.
• W2017689092 cites W2022812626 @default.
• W2017689092 cites W2036783526 @default.
• W2017689092 cites W2038645959 @default.
• W2017689092 cites W2046941586 @default.
• W2017689092 cites W2048219658 @default.
• W2017689092 cites W2049471263 @default.
• W2017689092 cites W2049855494 @default.
• W2017689092 cites W2053715317 @default.
• W2017689092 cites W2054384069 @default.
• W2017689092 cites W2058481677 @default.
• W2017689092 cites W2058807024 @default.
• W2017689092 cites W2064370579 @default.
• W2017689092 cites W2069890151 @default.
• W2017689092 cites W2071522110 @default.
• W2017689092 cites W2075647286 @default.
• W2017689092 cites W2078290497 @default.
• W2017689092 cites W2078619499 @default.
• W2017689092 cites W2081052410 @default.
• W2017689092 cites W2083355945 @default.
• W2017689092 cites W2096209718 @default.
• W2017689092 cites W2097792351 @default.
• W2017689092 cites W2108246412 @default.
• W2017689092 cites W2112966028 @default.
• W2017689092 cites W2115438550 @default.
• W2017689092 cites W2131659142 @default.
• W2017689092 cites W2135280991 @default.
• W2017689092 cites W2136251662 @default.
• W2017689092 cites W2137751050 @default.
• W2017689092 cites W2150953991 @default.
• W2017689092 cites W2157881370 @default.
• W2017689092 cites W2164082066 @default.
• W2017689092 cites W2170498245 @default.
• W2017689092 cites W2267016591 @default.
• W2017689092 cites W2399950595 @default.
• W2017689092 cites W2412974818 @default.
• W2017689092 cites W2466852614 @default.
• W2017689092 cites W4232982909 @default.
• W2017689092 cites W4239510810 @default.
• W2017689092 cites W4376848438 @default.
• W2017689092 cites W7692620 @default.
• W2017689092 cites W3139562178 @default.
• W2017689092 doi "https://doi.org/10.1016/j.jneumeth.2015.01.022" @default.
• W2017689092 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25629798" @default.
• W2017689092 hasPublicationYear "2015" @default.
• W2017689092 type Work @default.
• W2017689092 sameAs 2017689092 @default.
• W2017689092 citedByCount "238" @default.
• W2017689092 countsByYear W20176890922015 @default.
• W2017689092 countsByYear W20176890922016 @default.
• W2017689092 countsByYear W20176890922017 @default.
• W2017689092 countsByYear W20176890922018 @default.
• W2017689092 countsByYear W20176890922019 @default.
• W2017689092 countsByYear W20176890922020 @default.
• W2017689092 countsByYear W20176890922021 @default.
• W2017689092 countsByYear W20176890922022 @default.
• W2017689092 countsByYear W20176890922023 @default.
• W2017689092 crossrefType "journal-article" @default.
• W2017689092 hasAuthorship W2017689092A5003921571 @default.
• W2017689092 hasAuthorship W2017689092A5018557661 @default.

• next