Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Obeid, Iyad"'
Autor:
Khalkhali, Vahid, Shawki, Nabila, Shah, Vinit, Golmohammadi, Meysam, Obeid, Iyad, Picone, Joseph
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 exploited to achieve accurate detection of seizure events. Most popula
Externí odkaz:
http://arxiv.org/abs/2202.07796
Publikováno v:
In Journal of Structural Biology: X December 2024 10
Autor:
Shah, Vinit, von Weltin, Eva, Lopez, Silvia, McHugh, James Riley, Veloso, Lily, Golmohammadi, Meysam, Obeid, Iyad, Picone, Joseph
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 paper, we describe the techniques used to develop TUSZ, evaluate their ef
Externí odkaz:
http://arxiv.org/abs/1801.08085
Publikováno v:
A. Harati, M. Golmohammadi, S. Lopez, I. Obeid and J. Picone, "Improved EEG event classification using differential energy," 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, 2015, pp. 1-4
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 wavelets are popular analysis techniques in many signal processing appl
Externí odkaz:
http://arxiv.org/abs/1801.02477
Autor:
von Weltin, Eva, Ahsan, Tameem, Shah, Vinit, Jamshed, Dawer, Golmohammadi, Meysam, Obeid, Iyad, Picone, Joseph
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 portions of the record. A transient decrease in frequency, referred to as
Externí odkaz:
http://arxiv.org/abs/1801.02470
Autor:
Golmohammadi, Meysam, Ziyabari, Saeedeh, Shah, Vinit, Von Weltin, Eva, Campbell, Christopher, Obeid, Iyad, Picone, Joseph
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). The latter is the focus on this paper. A si
Externí odkaz:
http://arxiv.org/abs/1801.02471