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Found 10 papers, 0 papers with code
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning
Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced.
Improved EEG Event Classification Using Differential Energy
Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal.
Optimizing Channel Selection for Seizure Detection
However, when fewer electrodes are used, less spatial information is available, making it harder to detect artifacts.
The Temple University Hospital Seizure Detection Corpus
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.
An Analysis of Two Common Reference Points for EEGs
In this study, we explore the impact this variability has on machine learning performance.
Gated Recurrent Networks for Seizure Detection
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 electroencephalography (EEG).
Semi-automated Annotation of Signal Events in Clinical EEG Data
In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus.
Objective evaluation metrics for automatic classification of EEG events
In this paper, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance.
Deep Architectures for Automated Seizure Detection in Scalp EEGs
We have also evaluated our system on a held-out evaluation set based on the Duke University Seizure Corpus and demonstrate that performance trends are similar to the TUH EEG Seizure Corpus.
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures
Significance: The TUH EEG Corpus enables application of highly data consumptive machine learning algorithms to EEG analysis.
