49 citations · 156 across the 8 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures
Meysam Golmohammadi, Amir Hossein Harati Nejad Torbati, Silvia Lopez de Diego +2
Objective: A clinical decision support tool that automatically interprets EEGs can reduce time to diagnosis and enhance real-time applications such as ICU monitoring. Clinicians ha…
Objective evaluation metrics for automatic classification of EEG events
Saeedeh Ziyabari, Vinit Shah, Meysam Golmohammadi +2
The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics s…