Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies

Björn Schelter, Matthias Winterhalder, Thomas Maiwald, Armin Brandt, Ariane Schad, Jens Timmer, Andreas Schulze-Bonhage

Research output: Contribution to journalArticle

82 Citations (Scopus)

Abstract

Purpose: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle.

Methods: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics.

Results: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns.

Conclusions: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.

Original languageEnglish
Pages (from-to)2058-2070
Number of pages13
JournalEpilepsia
Volume47
Issue number12
Early online date28 Nov 2006
DOIs
Publication statusPublished - Dec 2006

Keywords

  • seizure prediction
  • seizure anticipation
  • false predictions
  • phase synchronization
  • dynamic similarity index

Cite this

Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies. / Schelter, Björn ; Winterhalder, Matthias; Maiwald, Thomas; Brandt, Armin; Schad, Ariane; Timmer, Jens; Schulze-Bonhage, Andreas.

In: Epilepsia, Vol. 47, No. 12, 12.2006, p. 2058-2070.

Research output: Contribution to journalArticle

Schelter, Björn ; Winterhalder, Matthias ; Maiwald, Thomas ; Brandt, Armin ; Schad, Ariane ; Timmer, Jens ; Schulze-Bonhage, Andreas. / Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies. In: Epilepsia. 2006 ; Vol. 47, No. 12. pp. 2058-2070.
@article{df0155be014941e586f95d2e2f56f720,
title = "Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies",
abstract = "Purpose: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle.Methods: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics.Results: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86{\%} of all false predictions occurred during sleep for the dynamic similarity index and 68{\%} for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50{\%} without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns.Conclusions: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.",
keywords = "seizure prediction, seizure anticipation, false predictions, phase synchronization, dynamic similarity index",
author = "Bj{\"o}rn Schelter and Matthias Winterhalder and Thomas Maiwald and Armin Brandt and Ariane Schad and Jens Timmer and Andreas Schulze-Bonhage",
year = "2006",
month = "12",
doi = "10.1111/j.1528-1167.2006.00848.x",
language = "English",
volume = "47",
pages = "2058--2070",
journal = "Epilepsia",
issn = "0013-9580",
publisher = "Wiley-Blackwell",
number = "12",

}

TY - JOUR

T1 - Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies

AU - Schelter, Björn

AU - Winterhalder, Matthias

AU - Maiwald, Thomas

AU - Brandt, Armin

AU - Schad, Ariane

AU - Timmer, Jens

AU - Schulze-Bonhage, Andreas

PY - 2006/12

Y1 - 2006/12

N2 - Purpose: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle.Methods: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics.Results: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns.Conclusions: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.

AB - Purpose: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle.Methods: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics.Results: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns.Conclusions: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.

KW - seizure prediction

KW - seizure anticipation

KW - false predictions

KW - phase synchronization

KW - dynamic similarity index

U2 - 10.1111/j.1528-1167.2006.00848.x

DO - 10.1111/j.1528-1167.2006.00848.x

M3 - Article

VL - 47

SP - 2058

EP - 2070

JO - Epilepsia

JF - Epilepsia

SN - 0013-9580

IS - 12

ER -