The Temple University Hospital EEG Data Corpus

Obeid, Iyad; Picone, J.

doi:10.3389/fnins.2016.00196

Cited by 335 publications

(207 citation statements)

References 12 publications

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“…It is taken from the TUH EEG Data Corpus which contains over 16000 clinical recordings of more than 10000 subjects from over 12 years [6]. The Abnormal Corpus contains 3017 recordings, 1529 of which were labeled normal and 1488 of which were labeled pathological.…”

Section: G Datasetmentioning

confidence: 99%

“…So far, the applications were mostly limited to specific diagnoses such as Alzheimer's disease regression, neural networks, and more. This large variety of used methods indicates that the search for the best decoding approach for diverse types of EEG diagnosis is still ongoing.To overcome the lack of large datasets representative of the variety of EEG-diagnosable diseases and the heterogeneity of clinical populations, the Temple University Hospital (TUH) has published an unprecedented public dataset of clinical EEG recordings [6]. From this dataset with over 16000 clinical recordings, the TUH Abnormal EEG Corpus with about 3000 recordings has been created specifically to foster the development of methods for distinguishing pathological from normal EEG.…”

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confidence: 99%

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Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Schirrmeister

Gemein

Eggensperger

et al. 2017

2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

146

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Abstract-We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ≈85% vs. ≈79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.I. I Electroencephalography (EEG) is widely used in clinical practice because of its low cost and its lack of side effects due to its noninvasive nature. It is important both as a screening method as well as for hypothesis-based diagnostics, e.g., in epilepsy or stroke. One of the main limitations of using EEG for diagnostics is the required time and specialized knowledge of experts that need to be well-trained on EEG diagnostics to reach reliable results. Therefore, a machine-learning approach that aids in the diagnostic process could make EEG diagnosis more widely accessible, reduce time and effort for clinicians and potentially make diagnoses more accurate.In recent years researchers have increasingly addressed the field of computer-aided EEG diagnosis. So far, the applications were mostly limited to specific diagnoses such as Alzheimer's disease regression, neural networks, and more. This large variety of used methods indicates that the search for the best decoding approach for diverse types of EEG diagnosis is still ongoing.To overcome the lack of large datasets representative of the variety of EEG-diagnosable diseases and the heterogeneity of clinical populations, the Temple University Hospital (TUH) has published an unprecedented public dataset of clinical EEG recordings [6]. From this dataset with over 16000 clinical recordings, the TUH Abnormal EEG Corpus with about 3000 recordings has been created specifically to foster the development of methods for distinguishing pathological from normal EEG. Due to its size and rich annotatio...

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Section: G Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Schirrmeister

Gemein

Eggensperger

et al. 2017

2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

146

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“…The motivation behind our work is to automate this first step of interpretation. We do so using a recently released dataset known as the TUH Abnormal EEG Corpus, which is the largest of its type to date [17] and freely available at [1]. Inspired by successess in time-domain signal classification, we explore recurrent neural network (RNN) architectures using the raw EEG time-series signal as input.…”

Section: Introductionmentioning

confidence: 99%

ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

Roy

Kiral-Kornek

Harrer

2019

Lecture Notes in Computer Science

133

102

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Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated analysis of EEG data offers the potential to improve the quality of patient care by shortening the time to diagnosis and reducing manual error. In this paper, we focus on one of the first steps in interpreting an EEG session -identifying whether the brain activity is abnormal or normal. To address this specific task, we propose a novel recurrent neural network (RNN) architecture termed ChronoNet which is inspired by recent developments from the field of image classification and designed to work efficiently with EEG data. ChronoNet is formed by stacking multiple 1D convolution layers followed by deep gated recurrent unit (GRU) layers where each 1D convolution layer uses multiple filters of exponentially varying lengths and the stacked GRU layers are densely connected in a feed-forward manner. We used the recently released TUH Abnormal EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous studies using this dataset, ChronoNet directly takes time-series EEG as input and learns meaningful representations of brain activity patterns. ChronoNet outperforms previously reported results on this dataset thereby setting a new benchmark. Furthermore, we demonstrate the domain-independent nature of ChronoNet by successfully applying it to classify speech commands.

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“…Schirrmeister et al recently described a convolutional neural network (CNN) that classifies EEGs as normal vs abnormal, using 20 minutes of 21-channel EEG from 3017 subjects in the TUH dataset (Obeid and Picone 2016). The network was trained in an end-to-end manner without hand-crafted features.…”

Section: Introductionmentioning

confidence: 99%

Detecting abnormal electroencephalograms using deep convolutional networks

Leeuwen

Sun

Tabaeizadeh

et al. 2019

Clinical Neurophysiology

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Objectives: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors. Methods: We train a deep convolutional neural network on a heterogeneous set of 8,522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage. Results: The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC=0.924), and both age and sleep stages (AUC= 0.925), though not statistically significant. Conclusions: The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance. Significance: Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.

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The Temple University Hospital EEG Data Corpus

Cited by 335 publications

References 12 publications

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

Detecting abnormal electroencephalograms using deep convolutional networks

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