most citedImproved EEG Event Classification Using Differential Energy

49 citations · 126 across the 6 of their papers we have counts for

collaborators

9 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.SP201849 cited

Improved EEG Event Classification Using Differential Energy

Amir Harati, Meysam Golmohammadi, Silvia Lopez +2

Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or w…

eess.SP20182 cited

Semi-automated Annotation of Signal Events in Clinical EEG Data

Scott Yang, Silvia Lopez, Meysam Golmohammadi +2

To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from…

eess.SP201828 cited

An Analysis of Two Common Reference Points for EEGs

Silvia Lopez, Aaron Gross, Scott Yang +3

Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical…

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…