Seizure detection: evaluation of the Reveal algorithm
Objective: The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts. Methods: 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to s...
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| Published in | Clinical neurophysiology Vol. 115; no. 10; pp. 2280 - 2291 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Shannon
Elsevier Ireland Ltd
01.10.2004
Elsevier Science |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1388-2457 1872-8952 |
| DOI | 10.1016/j.clinph.2004.05.018 |
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| Abstract | Objective: The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.
Methods: 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.
Results: Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.
Conclusions: This study validates the Reveal algorithm, and shows it to compare favorably with other methods.
Significance: Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. |
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| AbstractList | Objective: The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.
Methods: 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.
Results: Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.
Conclusions: This study validates the Reveal algorithm, and shows it to compare favorably with other methods.
Significance: Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts. 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm. Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively. This study validates the Reveal algorithm, and shows it to compare favorably with other methods. Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.OBJECTIVEThe aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.METHODS672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.RESULTSReveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.This study validates the Reveal algorithm, and shows it to compare favorably with other methods.CONCLUSIONSThis study validates the Reveal algorithm, and shows it to compare favorably with other methods.Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit.SIGNIFICANCEImproved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. |
| Author | Emerson, Ronald G. Wilson, Scott B. Scheuer, Mark L. Gabor, Andrew J. |
| Author_xml | – sequence: 1 givenname: Scott B. surname: Wilson fullname: Wilson, Scott B. email: scottw@eeg-persyst.com organization: Persyst Development Corporation, 1060 Sandretto Drive, Suite E2, Prescott, AZ 86305, USA – sequence: 2 givenname: Mark L. surname: Scheuer fullname: Scheuer, Mark L. organization: Epilepsy Lab, University of Pittsburgh, 811 Liliane Kaufman Building, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA – sequence: 3 givenname: Ronald G. surname: Emerson fullname: Emerson, Ronald G. organization: Department of Neurology, Columbia University, Neurological Institute, 710 West 168 Street, New York, NY 10032, USA – sequence: 4 givenname: Andrew J. surname: Gabor fullname: Gabor, Andrew J. organization: Department of Neurology, UC Davis, 1515 Newton Court, Room 510, Davis, CA 95616, USA |
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| Cites_doi | 10.1016/S0013-4694(98)00043-1 10.1016/S0013-4694(97)00092-8 10.1016/0013-4694(92)90175-H 10.1016/S1388-2457(98)00023-6 10.1145/569147.569151 10.1016/S1388-2457(00)00543-5 10.1109/10.720198 10.1016/S1388-2457(03)00212-8 10.1016/S0013-4694(98)00024-8 10.1016/0013-4694(95)00221-9 |
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| Keywords | Electroencephalography Neural network Seizure detection Clustering Algorithm Matching pursuit Cluster analysis Human Evaluation Nervous system diseases Epilepsy Epileptic seizure Intensive care unit Cerebral disorder Electrodiagnosis Central nervous system disease Signal detection Monitoring |
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| SubjectTerms | Adolescent Adult Algorithm Algorithms Biological and medical sciences Child Child, Preschool Cluster Analysis Clustering Electrodiagnosis. Electric activity recording Electroencephalography Electroencephalography - statistics & numerical data Expert Systems False Positive Reactions Female Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy Humans Infant Investigative techniques, diagnostic techniques (general aspects) Male Matching pursuit Medical sciences Middle Aged Monitoring, Ambulatory Nervous system Nervous system (semeiology, syndromes) Neural network Neurology ROC Curve Seizure detection Seizures - diagnosis Seizures - physiopathology |
| Title | Seizure detection: evaluation of the Reveal algorithm |
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