Skip to main navigation
Skip to search
Skip to main content
Manipal Academy of Higher Education, Manipal, India Home
Help & FAQ
Home
Profiles
Research units
Research output
Equipment
Search by expertise, name or affiliation
Automated classification of depression electroencephalographic signals using discrete cosine transform and nonlinear dynamics
G. Muralidhar Bairy
, Shreya Bhat
, Lim Wei Jie Eugene
, U. C. Niranjan
, Subha D. Puthankattil
, Paul K. Joseph
Department of Biomedical Engineering, Manipal Institute of Technology, Manipal
Research output
:
Contribution to journal
›
Article
›
peer-review
24
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Automated classification of depression electroencephalographic signals using discrete cosine transform and nonlinear dynamics'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
INIS
classification
100%
nonlinear problems
100%
dynamics
100%
signals
100%
vectors
40%
dimensions
40%
brain
20%
tools
20%
electrochemistry
20%
correlations
20%
values
20%
sensitivity
20%
specificity
20%
accuracy
20%
variations
20%
processing
20%
entropy
20%
decision tree analysis
20%
fluctuations
20%
lyapunov method
20%
mental disorders
20%
fractals
20%
Computer Science
Discrete Cosine Transform
100%
Support Vector Machine
66%
Decision Trees
33%
Radial Basis Function
33%
Transform Coefficient
33%
correlation dimension
33%
Physical State
33%
Emotional State
33%
Nave Bayes
33%
Classification Accuracy
33%
Bayes Classifier
33%
Characteristic Feature
33%
Lyapunov Exponent
33%
Fractal Dimension
33%