L
Lee M. Hively
Researcher at Oak Ridge National Laboratory
Publications - 77
Citations - 2080
Lee M. Hively is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Electroencephalography & Nonlinear system. The author has an hindex of 21, co-authored 75 publications receiving 1957 citations. Previous affiliations of Lee M. Hively include Martin Marietta Materials, Inc. & Battelle Memorial Institute.
Papers
More filters
Journal ArticleDOI
Detecting dynamical changes in time series using the permutation entropy
TL;DR: It is shown that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes.
Patent
Epileptic seizure prediction by nonlinear methods
TL;DR: In this paper, a method and apparatus for automatically predicting epileptic seizures and to monitor and analyze brain wave (EEG or MEG) signals was proposed, using chaotic time series analysis tools.
Journal ArticleDOI
Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease
Joseph McBride,Xiaopeng Zhao,Xiaopeng Zhao,Nancy B. Munro,Charles D. Smith,Gregory A. Jicha,Lee M. Hively,Lucas S. Broster,Frederick A. Schmitt,Richard J. Kryscio,Yang Jiang +10 more
TL;DR: The results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.
Patent
Method and apparatus for extraction of low-frequency artifacts from brain waves for alertness detection
Ned E. Clapp,Lee M. Hively +1 more
TL;DR: In this article, the authors automatically detect alertness in humans by monitoring and analyzing brain wave signals (EEG or MEG) from the subject, digitizing the data, separating artifact data from raw data, and comparing trends in F-data alertness indicators.
Patent
Condition assessment of nonlinear processes
TL;DR: In this paper, the authors presented a reliable technique for measuring condition change in nonlinear data such as brain waves, filtering and discretizing the nonlinear EEG data into windowed data sets, where the system dynamics within each data set is represented by a sequence of connected phase-space points, and a distribution function is derived.