On the proper selection of preictal period for seizure prediction

Bandarabadi, Mojtaba; Rasekhi, Jalil; Teixeira, César; Karami, Mohammad Reza; Dourado, António

doi:10.1016/j.yebeh.2015.03.010

Cited by 60 publications

(47 citation statements)

References 49 publications

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“…For example, Bandarabadi et al [17] present a statistical analysis for the optimal choice of PP and conclude that optimal PP is seizure-specific , i.e., it is not possible to select a single good PP for future data.…”

Section: Problem Formalization For Seizure Predictionmentioning

confidence: 99%

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

Shiao

Cherkassky

Lee

et al. 2017

IEEE Trans. Biomed. Eng.

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Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages.

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Section: Problem Formalization For Seizure Predictionmentioning

confidence: 99%

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

Shiao

Cherkassky

Lee

et al. 2017

IEEE Trans. Biomed. Eng.

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“…Practically, creating each new dataset could require physician-months or physician-years of labeling time, making repeated re-labeling campaigns a substantial diversion of resources. This problem is particularly salient for automated EEG monitoring, as achieving reliably high seizure detection performance across different patients even within the same population has proven difficult [24][25][26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

“…The weak annotations in those signals had an overall precision of 0.37 and recall of 0.45. While most previous studies [24][25][26][27][28] use clinician-generated labels for training, we propose to directly use these weak annotations for model supervision instead (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have previously tackled the problem of detecting the transition from the preictal period, which precedes seizure to the ictal period that is characterized by seizure. This is a challenging task because the transition between the preictal and ictal periods is often subtle 24 . Further, because seizures have patient-specific characteristics, many detection algorithms require training on each individual patient [24][25][26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

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Weak supervision as an efficient approach for automated seizure detection in electroencephalography

et al. 2020

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Automated seizure detection from electroencephalography (EEG) would improve the quality of patient care while reducing medical costs, but achieving reliably high performance across patients has proven difficult. Convolutional Neural Networks (CNNs) show promise in addressing this problem, but they are limited by a lack of large labeled training datasets. We propose using imperfect but plentiful archived annotations to train CNNs for automated, real-time EEG seizure detection across patients. While these weak annotations indicate possible seizures with precision scores as low as 0.37, they are commonly produced in large volumes within existing clinical workflows by a mixed group of technicians, fellows, students, and board-certified epileptologists. We find that CNNs trained using such weak annotations achieve Area Under the Receiver Operating Characteristic curve (AUROC) values of 0.93 and 0.94 for pediatric and adult seizure onset detection, respectively. Compared to currently deployed clinical software, our model provides a 31% increase (18 points) in F1-score for pediatric patients and a 17% increase (11 points) for adult patients. These results demonstrate that weak annotations, which are sustainably collected via existing clinical workflows, can be leveraged to produce clinically useful seizure detection models.

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“…As usual, 60% of the Top downloaded papers are review articles, but interestingly enough, the first one is a novel statistical approach for a proper selection of the preictal period, which can also be considered a potential measure of predictability of a seizure (14). Four papers are part of a Special Issue on "Status Epilepticus" from the 5 th London -Innsbruck Colloquium on Status Epilepticus and Acute Seizures (15).…”

mentioning

confidence: 99%

New trends and hot topics in epileptology: An analysis of top articles published in Epilepsy & Behavior in 2015

Mula

2016

Epilepsy & Behavior

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On the proper selection of preictal period for seizure prediction

Cited by 60 publications

References 49 publications

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

Weak supervision as an efficient approach for automated seizure detection in electroencephalography

New trends and hot topics in epileptology: An analysis of top articles published in Epilepsy & Behavior in 2015

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