Vakorin et al., 2013 - Google Patents
Exploring age-related changes in dynamical non-stationarity in electroencephalographic signals during early adolescenceVakorin et al., 2013
View HTML- Document ID
- 6629385569354233977
- Author
- Vakorin V
- McIntosh A
- Mišić B
- Krakovska O
- Poulsen C
- Martinu K
- Paus T
- Publication year
- Publication venue
- PloS one
External Links
Snippet
Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these …
- 210000004556 Brain 0 abstract description 47
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0476—Electroencephalography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0531—Measuring skin impedance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0488—Electromyography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/04012—Analysis of electro-cardiograms, electro-encephalograms, electro-myograms
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chiarion et al. | Connectivity analysis in EEG data: a tutorial review of the state of the art and emerging trends | |
Li et al. | Detection of epileptic seizure based on entropy analysis of short-term EEG | |
Sun et al. | Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics | |
Deco et al. | Rethinking segregation and integration: contributions of whole-brain modelling | |
Ahmadlou et al. | Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder | |
Daly et al. | Automated artifact removal from the electroencephalogram: a comparative study | |
Cui et al. | Inferring cortical variability from local field potentials | |
Hu et al. | Causality analysis of neural connectivity: critical examination of existing methods and advances of new methods | |
Sargolzaei et al. | Scalp EEG brain functional connectivity networks in pediatric epilepsy | |
Pagnotta et al. | Time-varying MVAR algorithms for directed connectivity analysis: Critical comparison in simulations and benchmark EEG data | |
Bialonski et al. | Assortative mixing in functional brain networks during epileptic seizures | |
Tagliazucchi et al. | Multimodal imaging of dynamic functional connectivity | |
Vakorin et al. | Exploring age-related changes in dynamical non-stationarity in electroencephalographic signals during early adolescence | |
Battaglia et al. | Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex | |
Lehnertz et al. | Capturing time-varying brain dynamics | |
Lee et al. | Assessing levels of consciousness with symbolic analysis | |
Murin et al. | SozRank: A new approach for localizing the epileptic seizure onset zone | |
Breakspear | The nonlinear theory of schizophrenia | |
Storti et al. | Brain network connectivity and topological analysis during voluntary arm movements | |
Davoudi et al. | Frequency–amplitude coupling: a new approach for decoding of attended features in covert visual attention task | |
Alexander et al. | Large-scale cortical travelling waves predict localized future cortical signals | |
Souza et al. | Synchronization and propagation of global sleep spindles | |
Alqahtani et al. | Classifying electroencephalogram signals using an innovative and effective machine learning method based on chaotic elephant herding optimum | |
Zhang et al. | EEG source‐space synchrostate transitions and Markov modeling in the math‐gifted brain during a long‐chain reasoning task | |
Alavash et al. | Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making |