Performance Reassessment of a Real-time Seizure-detection Algorithm on Long ECoG Series

Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed‐loop studies to block seizures in...

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Published inEpilepsia (Copenhagen) Vol. 43; no. 12; pp. 1522 - 1535
Main Authors Osorio, Ivan, Frei, Mark G., Giftakis, Jon, Peters, Tom, Ingram, Jeff, Turnbull, Mary, Herzog, Michele, Rise, Mark T., Schaffner, Scott, Wennberg, Richard A., Walczak, Thaddeus S., Risinger, Michael W., Ajmone‐Marsan, Cosimo
Format Journal Article
LanguageEnglish
Published Boston, MA, USA Blackwell Science Inc 01.12.2002
Blackwell
Subjects
Online AccessGet full text
ISSN0013-9580
1528-1167
DOI10.1046/j.1528-1157.2002.11102.x

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Abstract Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed‐loop studies to block seizures in humans. Methods: Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off‐line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated. Results: Fourteen subjects met inclusion criteria. Generic algorithm “relative sensitivity” for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7–66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7–4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs. Conclusions: The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed‐loop therapy studies.
AbstractList Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed‐loop studies to block seizures in humans. Methods: Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off‐line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated. Results: Fourteen subjects met inclusion criteria. Generic algorithm “relative sensitivity” for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7–66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7–4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs. Conclusions: The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed‐loop therapy studies.
Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans. Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated. Fourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs. The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies.
Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed‐loop studies to block seizures in humans. Methods: Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off‐line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated. Results: Fourteen subjects met inclusion criteria. Generic algorithm “relative sensitivity” for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7–66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7–4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs. Conclusions: The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed‐loop therapy studies.
Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans.PURPOSEAutomated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans.Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated.METHODSUp to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated.Fourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs.RESULTSFourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs.The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies.CONCLUSIONSThe generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies.
Author HERZOG Michele
WALCZAK Thaddeus S.
AJMONE-MARSAN Cosimo
FREI Mark G.
INGRAM Jeff
OSORIO Ivan
RISINGER Michael W.
RISE Mark T.
GIFTAKIS Jon
SCHAFFNER Scott
WENNBERG Richard A.
PETERS Tom
TURNBULL Mary
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Keywords Human
Electrodiagnosis
Convulsion
Real-time-Seizure-Detection- Algorithm-Warning
Neurological disorder
Medical screening
Real time
Algorithm
Electrocorticography
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PublicationTitle Epilepsia (Copenhagen)
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Snippet Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by...
Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by...
Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a...
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SubjectTerms Adolescent
Adult
Algorithm
Algorithms
Biological and medical sciences
Cerebral Cortex - physiopathology
Detection
Diagnosis, Computer-Assisted
Electrodes, Implanted
Electrodiagnosis. Electric activity recording
Electroencephalography
Epilepsies, Partial - diagnosis
Epilepsies, Partial - physiopathology
Epilepsy, Complex Partial - diagnosis
Epilepsy, Complex Partial - physiopathology
Epilepsy, Tonic-Clonic - diagnosis
Epilepsy, Tonic-Clonic - physiopathology
Evoked Potentials - physiology
Female
Fourier Analysis
Humans
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Middle Aged
Monitoring, Ambulatory
Nervous system
Observer Variation
Reaction Time - physiology
Real-time
Seizure
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Warning
Title Performance Reassessment of a Real-time Seizure-detection Algorithm on Long ECoG Series
URI https://cir.nii.ac.jp/crid/1571698599797659392
https://onlinelibrary.wiley.com/doi/abs/10.1046%2Fj.1528-1157.2002.11102.x
https://www.ncbi.nlm.nih.gov/pubmed/12460255
https://www.proquest.com/docview/72723700
Volume 43
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