The Temple University Hospital Seizure Detection Corpus
نویسندگان
چکیده
The electroencephalogram (EEG), which has been in clinical use for over 70 years, is still an essential tool for diagnosis of neural functioning (Kennett, 2012). Well-known applications of EEGs include identification of epilepsy and epileptic seizures, anoxic and hypoxic damage to the brain, and identification of neural disorders such as hemorrhagic stroke, ischemia and toxic metabolic encephalopathy (Drury, 1988). More recently there has been interest in diagnosing Alzheimer’s (Tsolaki et al., 2014), head trauma (Rapp et al., 2015) and sleep disorders (Younes, 2017). Many of these clinical applications now involve the collection of large amounts of data (e.g., 72-hour continuous EEG recordings), which makes manual interpretation challenging. Similarly, the increased use of EEGs in critical care has created a significant demand for high-performance automatic interpretation software (e.g., real-time seizure detection).
منابع مشابه
Deep Architectures for Automated Seizure Detection in Scalp EEGs
Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign variants (e.g., slowing). Commercially available systems suffer from unacceptably high false alarm ...
متن کاملErratum to “Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients” [Seizure 26 (2015) 43–48]
Jesper Jeppesen *, Sándor Beniczky , Peter Johansen , Per Sidenius , Anders Fuglsang-Frederiksen a Department of Neurophysiology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visby Álle 5, 4293 Dianalund, Denmark Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark Department ...
متن کاملNewborn EEG Seizure Detection Based on Interspike Space Distribution in the Time-Frequency Domain
This paper presents a new time-frequency based EEG seizure detection method. This method uses the distribution of interspike intervals as a criterion for discriminating between seizure and nonseizure activities. To detect spikes in the EEG, the signal is mapped into the time-frequency domain. The high instantaneous energy of spikes is reflected as a localized energy in time-frequency domain. Hi...
متن کاملEpileptic seizure detection based on The Limited Penetrable visibility graph algorithm and graph properties
Introduction: Epileptic seizure detection is a key step for both researchers and epilepsy specialists for epilepsy assessment due to the non-stationariness and chaos in the electroencephalogram (EEG) signals. Current research is directed toward the development of an efficient method for epilepsy or seizure detection based the limited penetrable visibility graph (LPVG) algorith...
متن کامل