Yan Liu1,2, Jie Gao3, Wei Cao4, Longxiao Wei1, Yanyang Mao5, Weimin Liu6, Wei Wang1, Zhenling Liu7. 1. Department of Nuclear Medicine, The Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China. 2. Department of Radiology, Gem Flower Changqing Hospital, Xi'an 710018, China. 3. Department of Radiology, Binzhou People's Hospital, Binzhou 256600, China. 4. Department of Interventional Radiology, The Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China. 5. Department of Cardiovascular, Yan'an Traditional Chinese Medicine Hospital, Yan'an 716000, China. 6. Department of Interventional Radiology, Jilin People's Hospital, Jilin 132000, China. 7. Department of Electrical Engineering and Computer Sciences, University of California, Irvine, CA, USA.
Abstract
BACKGROUND: We demonstrate an innovative approach of automated sleep recording formed on the electroencephalogram (EEG) with one channel. METHODS: In this study, double-density dual-tree discrete wavelet transformation (DDDTDWT) was used for decomposing the image, and marginal Fisher analysis (MFA) was used for reducing the dimension. A proposed model on unprocessed EEG models was used on monitored training of 5-group sleep phase forecasting. RESULTS: Our network includes a 14-row structure, and a 30-s period was extracted as input in order to be categorized which is followed by second and third period prior to the first 30-s period. Another consecutive period for temporal tissue was added which is not required to a signal preprocess and attribute data derivation phase. Our means of evaluating and improving our approach was to use input from the Sleep Heart Health Study (SHHS), which is a large study field aimed at using research from numerous centers and people and which studies the records of specialist-rated polysomnography (PSG). Performance measures could reach the desired level, which is a precision of 0.87 and a Cohen's kappa of 0.81. CONCLUSIONS: The use of a large, collaborative study of specialist graders can enhance the likelihood of good globalization. Overall, the novel approach learned by our network showcases the models based on each category. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: We demonstrate an innovative approach of automated sleep recording formed on the electroencephalogram (EEG) with one channel. METHODS: In this study, double-density dual-tree discrete wavelet transformation (DDDTDWT) was used for decomposing the image, and marginal Fisher analysis (MFA) was used for reducing the dimension. A proposed model on unprocessed EEG models was used on monitored training of 5-group sleep phase forecasting. RESULTS: Our network includes a 14-row structure, and a 30-s period was extracted as input in order to be categorized which is followed by second and third period prior to the first 30-s period. Another consecutive period for temporal tissue was added which is not required to a signal preprocess and attribute data derivation phase. Our means of evaluating and improving our approach was to use input from the Sleep Heart Health Study (SHHS), which is a large study field aimed at using research from numerous centers and people and which studies the records of specialist-rated polysomnography (PSG). Performance measures could reach the desired level, which is a precision of 0.87 and a Cohen's kappa of 0.81. CONCLUSIONS: The use of a large, collaborative study of specialist graders can enhance the likelihood of good globalization. Overall, the novel approach learned by our network showcases the models based on each category. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: A L Goldberger; L A Amaral; L Glass; J M Hausdorff; P C Ivanov; R G Mark; J E Mietus; G B Moody; C K Peng; H E Stanley Journal: Circulation Date: 2000-06-13 Impact factor: 29.690
Authors: Scott D Solomon; Nagesh Anavekar; Hicham Skali; John J V McMurray; Karl Swedberg; Salim Yusuf; Christopher B Granger; Eric L Michelson; Duolao Wang; Stuart Pocock; Marc A Pfeffer Journal: Circulation Date: 2005-12-05 Impact factor: 29.690
Authors: Yuan Yuan; Eliezer M Van Allen; Larsson Omberg; Nikhil Wagle; Ali Amin-Mansour; Artem Sokolov; Lauren A Byers; Yanxun Xu; Kenneth R Hess; Lixia Diao; Leng Han; Xuelin Huang; Michael S Lawrence; John N Weinstein; Josh M Stuart; Gordon B Mills; Levi A Garraway; Adam A Margolin; Gad Getz; Han Liang Journal: Nat Biotechnol Date: 2014-06-22 Impact factor: 54.908