The TUH EEG CORPUS: A big data resource for automated EEG interpretation

Harati, Amir; Lopez, S.; Obeid, Iyad; Picone, J.; Jacobson, Mercedes P.; Tobochnik, Steven

doi:10.1109/spmb.2014.7002953

Cited by 83 publications

(57 citation statements)

References 4 publications

(6 reference statements)

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“…Zhang et al [140] pointed out that the tasks of the visual analytics for commercial systems can be divided into four categories which are exploration, dashboards, reporting, and alerting. The study [141] showed that the interface for electroencephalography (EEG) interpretation is another noticeable research issue in big data analytics. The user interface for cloud system [142,143] is the recent trend for big data analytics.…”

Section: Output the Results Of Big Data Analysismentioning

confidence: 99%

Big data analytics: a survey

Tsai

Lai

Chao

et al. 2015

Journal of Big Data

617

310

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Section: Output the Results Of Big Data Analysismentioning

confidence: 99%

Big data analytics: a survey

Tsai

Lai

Chao

et al. 2015

Journal of Big Data

617

310

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“…Many authors concluded their paper suggesting that having access to more data would most likely improve the performance of their models. With large datasets becoming public, such as the TUH Dataset [65] and the National Sleep Research Resource [217], deeper architectures similar to the ones used in computer vision might become increasingly usable. However, it is important to note that the availability of data is quite different across domains.…”

Section: Datamentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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“…In this study we have focused on the TUH EEG Corpus [5] for evaluation. TUH EEG is the world’s largest publicly available database of clinical EEG data, comprising more than 28,000 EEG records and over 15,000 patients.…”

Section: Experimental Designmentioning

confidence: 99%

Automated identification of abnormal adult EEGs

Lopez

Suárez

Jungreis

et al. 2015

2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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The interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiners. Though interrater agreement on critical events such as seizures is high, it is much lower on subtler events (e.g., when there are benign variants). The process used by an expert to interpret an EEG is quite subjective and hard to replicate by machine. The performance of machine learning technology is far from human performance. We have been developing an interpretation system, AutoEEG, with a goal of exceeding human performance on this task. In this work, we are focusing on one of the early decisions made in this process – whether an EEG is normal or abnormal. We explore two baseline classification algorithms: k-Nearest Neighbor (kNN) and Random Forest Ensemble Learning (RF). A subset of the TUH EEG Corpus was used to evaluate performance. Principal Components Analysis (PCA) was used to reduce the dimensionality of the data. kNN achieved a 41.8% detection error rate while RF achieved an error rate of 31.7%. These error rates are significantly lower than those obtained by random guessing based on priors (49.5%). The majority of the errors were related to misclassification of normal EEGs.

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The TUH EEG CORPUS: A big data resource for automated EEG interpretation

Cited by 83 publications

References 4 publications

Big data analytics: a survey

Big data analytics: a survey

Deep learning-based electroencephalography analysis: a systematic review

Automated identification of abnormal adult EEGs

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