most citedDeep Architectures for Automated Seizure Detection in Scalp EEGs

46 citations · 46 across the 2 of their papers we have counts for

collaborators

5 papers

q-bio.QM2018

The Temple University Hospital Seizure Detection Corpus

Vinit Shah, Eva von Weltin, Silvia Lopez +5

We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open source corpus of its type, and represents an accurate characterization of clinical conditions. In this pap…

eess.SP2018

Optimizing Channel Selection for Seizure Detection

Vinit Shah, Meysam Golmohammadi, Saeedeh Ziyabari +3

Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG…

eess.SP2018

Gated Recurrent Networks for Seizure Detection

Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah +4

Recurrent Neural Networks (RNNs) with sophisticated units that implement a gating mechanism have emerged as powerful technique for modeling sequential signals such as speech or ele…

eess.SP2018

Electroencephalographic Slowing: A Source of Error in Automatic Seizure Detection

Eva von Weltin, Tameem Ahsan, Vinit Shah +4

Although a seizure event represents a major deviation from a baseline electroencephalographic signal, there are features of seizure morphology that can be seen in non-epileptic por…

cs.LG201746 cited

Deep Architectures for Automated Seizure Detection in Scalp EEGs

Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah +3

Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to nois…