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Automatic detection of intraoperative neurological injury
Luke V. Williams
Published in IEEE, Piscataway, NJ, United States
1999
PMID: 10217886
Volume: 46
   
Issue: 4
Pages: 481 - 483
Abstract
Neurological injuries occurring during high-risk surgical procedures can be detected by monitoring intraoperative evoked potential signals. In this communication, an automatic injury detection algorithm is proposed in which the EP signal is modeled as a pole-zero filter and then the model parameters are applied as inputs to a classifier type neural network. A recognition rate of 96% is achieved using an experimental model of brain injury.Neurological injuries occurring during high-risk surgical procedures can be detected by monitoring intraoperative evoked potential signals. In this communication, an automatic injury detection algorithm is proposed in which the EP signal is modeled as a pole-zero filter and then the model parameters are applied as inputs to a classifier type neural network. A recognition rate of 96% is achieved using an experimental model of brain injury.
About the journal
JournalData powered by TypesetIEEE Transactions on Biomedical Engineering
PublisherData powered by TypesetIEEE, Piscataway, NJ, United States
ISSN00189294
Open AccessNo
Concepts (35)
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    Bioelectric potentials
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    BRAIN MODELS
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    Cosine transforms
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    Neural networks
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    Automatic detection
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    Discrete cosine transform
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    INTRAOPERATIVE NEUROLOGICAL INJURY
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    POLE ZERO MODEL
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    Neurology
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    Algorithm
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    Animal experiment
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    Article
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    AUTOANALYSIS
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    Biomedical engineering
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    BRAIN HYPOXIA
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    Brain injury
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    Brain ischemia
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    CAT
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    Evoked response
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    EVOKED SOMATOSENSORY RESPONSE
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    NERVE CELL NETWORK
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    Nonhuman
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    PEROPERATIVE COMPLICATION
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    Signal detection
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    Signal processing
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    Time
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    Algorithms
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    Animals
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    BRAIN INJURIES
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    CATS
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    EVOKED POTENTIALS, SOMATOSENSORY
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    Models, neurological
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    MONITORING, INTRAOPERATIVE
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    Neural networks (computer)
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    Signal processing, computer-assisted