The Temple University Hospital Seizure Detection Corpus

In this paper, we describe the techniques used to develop the TUH EEG Seizure Corpus (TUSZ), evaluate their effectiveness, and present some descriptive statistics on the resulting corpus.

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Published in:Frontiers in neuroinformatics 2018-11, Vol.12, p.83-83
Main Authors: Shah, Vinit, von Weltin, Eva, Lopez, Silvia, McHugh, James Riley, Veloso, Lillian, Golmohammadi, Meysam, Obeid, Iyad, Picone, Joseph
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
annotated data
Annotations
Artificial intelligence
Big Data
Convulsions & seizures
Deep learning
EEG
electroencephalogram
Electroencephalography
Hypoxia
Machine learning
Neuroscience
seizure detection
Seizures
Sleep
Software
Temporal-Spatial sequence data
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cited_by cdi_FETCH-LOGICAL-c490t-f9e6cd628eaddad0a3e3d556909393e32fbe0261009d7e807b71aabe07947f1a3
cites cdi_FETCH-LOGICAL-c490t-f9e6cd628eaddad0a3e3d556909393e32fbe0261009d7e807b71aabe07947f1a3
container_end_page 83
container_issue
container_start_page 83
container_title Frontiers in neuroinformatics
container_volume 12
creator Shah, Vinit
von Weltin, Eva
Lopez, Silvia
McHugh, James Riley
Veloso, Lillian
Golmohammadi, Meysam
Obeid, Iyad
Picone, Joseph
description In this paper, we describe the techniques used to develop the TUH EEG Seizure Corpus (TUSZ), evaluate their effectiveness, and present some descriptive statistics on the resulting corpus.
doi_str_mv 10.3389/fninf.2018.00083
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subjects Algorithms
Alzheimer's disease
annotated data
Annotations
Artificial intelligence
Big Data
Convulsions & seizures
Deep learning
EEG
electroencephalogram
Electroencephalography
Hypoxia
Machine learning
Neuroscience
seizure detection
Seizures
Sleep
Software
Temporal-Spatial sequence data
title The Temple University Hospital Seizure Detection Corpus
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