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Low complexity algorithm for seizure prediction using Adaboost
Manohar Ayinala,
Keshab K. Parhi
Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
27
Scopus citations
Overview
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Dive into the research topics of 'Low complexity algorithm for seizure prediction using Adaboost'. Together they form a unique fingerprint.
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Keyphrases
Seizure
100%
Seizure Prediction
100%
AdaBoost
100%
AdaBoost Algorithm
100%
Low-complexity Algorithms
100%
Patient-specific
50%
Implantable Devices
50%
Electroencephalogram
50%
Spectral Power
50%
False Alarm
50%
Feature Selection
50%
Low Computational Complexity
50%
Feature Classification
50%
Freiburg
50%
Low False Alarm Rate
50%
Base Classifier
50%
Linear Classifier
50%
Nonlinear Classifier
50%
Decision Stump
50%
Power Features
50%
Feature Ranking Methods
50%
Computer Science
Algorithmic Efficiency
100%
Linear Classifier
100%
Adaboost Algorithm
100%
Computational Complexity
50%
Feature Selection
50%
Base Classifier
50%
Classification Stage
50%
False Alarm Rate
50%
Engineering
Adaboost
100%
Feature Extraction
33%
Medical Implant
33%
Patient Specific
33%
False Alarm Rate
33%
Computational Complexity
33%
False Alarm
33%
Neuroscience
Seizure Prediction
100%
Electroencephalogram
50%