An algorithm for on-line detection of high frequency oscillations related to epilepsy

Abstract Recent studies suggest that the appearance of signals with high frequency oscillations components in specific regions of the brain is related to the incidence of epilepsy. These oscillations are in general small in amplitude and short in duration, making them difficult to identify. The anal...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2013-06, Vol.110 (3), p.354-360
Main Authors: López-Cuevas, Armando, Castillo-Toledo, Bernardino, Medina-Ceja, Laura, Ventura-Mejía, Consuelo, Pardo-Peña, Kenia
Format: Article
Language:English
Subjects:
Algorithms
Animals
Applied sciences
Approximate entropy
Artificial intelligence
Biological and medical sciences
Computer science
control theory
systems
Connectionism. Neural networks
Cubature Kalman Filter
Diagnosis, Computer-Assisted - statistics & numerical data
Electroencephalography - statistics & numerical data
Electrophysiological Phenomena
Epilepsy
Epilepsy - diagnosis
Epilepsy - physiopathology
Exact sciences and technology
Fast ripples
Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy
High frequency oscillations
Humans
Internal Medicine
Male
Medical sciences
Nervous system (semeiology, syndromes)
Neural Networks (Computer)
Neurology
Oscillometry - statistics & numerical data
Other
Rats
Rats, Wistar
Signal Processing, Computer-Assisted
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Summary:Abstract Recent studies suggest that the appearance of signals with high frequency oscillations components in specific regions of the brain is related to the incidence of epilepsy. These oscillations are in general small in amplitude and short in duration, making them difficult to identify. The analysis of these oscillations are particularly important in epilepsy and their study could lead to the development of better medical treatments. Therefore, the development of algorithms for detection of these high frequency oscillations is of great importance. In this work, a new algorithm for automatic detection of high frequency oscillations is presented. This algorithm uses approximate entropy and artificial neural networks to extract features in order to detect and classify high frequency components in electrophysiological signals. In contrast to the existing algorithms, the one proposed here is fast and accurate, and can be implemented on-line, thus reducing the time employed to analyze the experimental electrophysiological signals.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2013.01.014