1. Scalable EEG interpretation using Deep Learning and Schema Descriptors
Human Language Technology Research Institute
Iyad Obeid and Joseph Picone
The Neural Engineering Data Consortium
Temple University
Sanda Harabagiu
The Human Language Technology Research Institute
University of Texas at Dallas
Abstract
Focus on addressing a critical market gap in EEG technology – real-time seizure detection for intensive care unit (ICU) and epilepsy monitoring unit (EMU) applications.
The ability to auto-scan EEGs and predict seizures in advance will be a transformational clinical technology. Existing products that seek to increase accuracy and productivity via automatic analysis are limited by high rates of false positives, overwhelming healthcare providers with misleading information.
Leverages our unique big data resources, including the TUH EEG Corpus and publicly available medical article databases, and state of the art deep learning technology, to revolutionize the automated interpretation of sequentially organized biomedical signals such as EEGs.
Automatic Identification of Seizures From EEG Signals Only Is Challenging
Aim 4: Automatic Tagging of HADs
In order to be able to index the metadata provided by HADs and to provide searching capabilities, we will tag in HADs in EEG reports and biomedical scientific articles.
To produce the HAD tag automatically we will experiment with several deep learning frameworks (e.g., LSTM and CDNN techniques).
The HAD tags shall be used to identify patients that may develop seizures.
In addition, clinical decision support shall be provided by retrieving scientific articles that document the medical care of similar patients before, during and after their seizures.
Aim 1:
Automatic labeling of the TUH EEG Corpus for seizure events.
Annotating these events through a bootstrapping process involving a combination of active learning and manual review will result in a corpus suited for machine learning experiments and be of general use to the community.
Anticipated Outcomes
Aim 1 will result in a corpus suited for machine learning experiments that will be of general use to the community.
Aim 2 will characterize seizure detection accuracy as a function of latency on the TUH EEG Corpus.
Our goal in Aim 2 is to detect seizures before they occur, which we refer to as negative latency.
Aim 3 will develop HAD schema that will provide a scalable solution for representing and annotating EEG epileptiform activities, including those related to seizure prediction, both in biomedical and clinical documents.
Aim 4 will inform medical article retrieval by the HAD schema and its massive annotations produced automatically on the EEG data.
The deep learning methods developed will provide a scalable solution for annotations on big data, both clinical and biomedical.
Acknowledgements
Research reported in this poster was supported by  National Human Genome Research Institute of the National Institutes of Health under award number 3U01HG S1.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The TUH EEG Corpus development was sponsored by the Defense Advanced Research Projects Agency (DARPA), Temple University’s College of Engineering and Office of Research.
References
Obeid, I., & Picone, J. (2016). The Temple University Hospital EEG Data Corpus. Frontiers in Neuroscience, Section Neural Tech., 10,
R. Maldonado, T.R. Goodwin and S.M. Harabagiu, “Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification” AMIA-CRI 2017.
TUH EEG Seizure Detection Subset
Subset of the publicly available TUH EEG Corpus (
Evaluation Data:
50 patients, 232 sessions, 986 files
167 hours of data
Training Data (estimated):
250 patients, 1000 sessions, 5,000 files
750 hours of data
Every EEG event is manually validated by three neurologists
Seizure event annotations include:
start and stop times;
localization of a seizure (e.g., focal, generalized) with the appropriate channels marked;
type of seizure (e.g., simple partial, complex partial, tonic-clonic, gelastic, absence, atonic);
nature of the seizure (e.g., convulsive)
The non-seizure event annotations include:
artifacts which could be confused with seizure- like events such as ventilatory artifacts and lead artifacts;
non-epileptiform activity that may resemble epileptiform discharges, such as psychomotor variant, mu, breach rhythms and POSTS;
abnormal background which could be confused with seizure-like events (e.g. triphasics);
interictal and postictal states.
Data will be available in 1Q’2017.
Aim 2:
Application of deep learning sequential modeling techniques for EEGs.
Long Short-Term Memory (LSTM) techniques and Convolutional Deep Neural Networks (CDNN)
There is ample evidence that the onset of a seizure can be predicted as much as 30 minutes in advance by clinicians.
Aim 3:
Defining Hierarchical epileptiform Activity Descriptors (HAD) for EEGs.
Hierarchical Event Descriptors (HED) (available from have defined many types of EEG experimental events, no existing components of schema.org standardize the epileptiform activities and their attributes.
We fill this gap by generating a schema of Hierarchical epileptiform Activity Descriptors (HADs). which is populated using deep learning methods that take into account the representation of the schema nodes and relations.