Utility of invasive ictal EEG recordings in pre-surgical evaluation of patients with medically refractory temporal lobe epilepsy and normal MRI

Chung, Jeffrey M.; Meador, Kimford J.; Eisenschenk, Stephan; Ghacibeh, Georges A.; Vergara, Deborah; Eliashiv, Dawn; Roper, Steven N.

doi:10.1016/j.ijep.2015.04.001

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…In the past, seizure prediction studies using EEG signals have been limited due to insufficient standardized and qualified data [ 55 ]. The EEG data have often been acquired from Intensive Care Unit (ICU) [ 56 ], presurgical inpatient [ 57 ], animals [ 58 ], and implantable devices [ 59 ]. Data usefulness was limited by the relatively short recording duration and inter-ictal time sampling of patients’ and animal models’ concerns.…”

Section: Datasetmentioning

confidence: 99%

Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Wang

Mengoni

2022

Brain Inf.

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Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients’ clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient’s reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist’s when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.

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Section: Datasetmentioning

confidence: 99%

Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Wang

Mengoni

2022

Brain Inf.

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Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models

Wang

Mengoni

2021

Brain Informatics

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Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings

Baldassano

Brinkmann

Ung

et al. 2017

Brain

105

119

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There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.

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Utility of invasive ictal EEG recordings in pre-surgical evaluation of patients with medically refractory temporal lobe epilepsy and normal MRI

Abstract: 2016-12-23T18:44:59

Cited by 3 publications

References 33 publications

Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings

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