49 citations · 140 across the 7 of their papers we have counts for
7 papers · 1 filter
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning
Vahid Khalkhali, Nabila Shawki, Vinit Shah +3
Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be…
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…
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…
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…
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…
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…