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Your search keyword '"Bingol, Canan Aykut"' showing total 13 results
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13 results on '"Bingol, Canan Aykut"'

1. Clinical experience: Outcomes of mesenchymal stem cell transplantation in five stroke patients

Author

Ercelen, Nesrin, primary, Karasu, Nilgun, additional, Kahyaoglu, Bulent, additional, Cerezci, Onder, additional, Akduman, Rana Cagla, additional, Ercelen, Defne, additional, Erturk, Gizem, additional, Gulay, Gokay, additional, Alpaydin, Nagihan, additional, Boyraz, Gizem, additional, Monteleone, Berrin, additional, Kural, Zekiye, additional, Silek, Hakan, additional, Temur, Sibel, additional, and Bingol, Canan Aykut, additional

Published
2023
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10. P092 Intraoperative SEP monitoring

Author

Karlikaya, Geysu, primary, Citci, Beyza, additional, Kahriman, Ayca, additional, Ture, Hatice, additional, Ture, Ugur, additional, and Bingol, Canan Aykut, additional

Published
2008
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13. Seizure recognition on epilepsy feature tensor.

Author

Acar E, Bingol CA, Bingol H, Bro R, and Yener B

Subjects
Humans, Linear Models, Seizures classification, Seizures physiopathology, Sensitivity and Specificity, Electroencephalography methods, Models, Biological, Seizures diagnosis, Signal Processing, Computer-Assisted
Abstract

With a goal of automating visual analysis of electroencephalogram (EEG) data and assessing the performance of various features in seizure recognition, we introduce a mathematical model capable of recognizing patient-specific epileptic seizures with high accuracy. We represent multi-channel scalp EEG using a set of features. These features expected to have distinct trends during seizure and non-seizure periods include features from both time and frequency domains. The contributions of this paper are threefold. First, we rearrange multi-channel EEG signals as a third-order tensor called an Epilepsy Feature Tensor with modes: time epochs, features and electrodes. Second, we model the Epilepsy Feature Tensor using a multilinear regression model, i.e., Multilinear Partial Least Squares regression, which is the generalization of Partial Least Squares (PLS) regression to higher-order datasets. This two-step approach facilitates EEG data analysis from multiple electrodes represented by several features from different domains. Third, we identify which features are more significant for seizure recognition. Our results based on the analysis of 19 seizures from 5 epileptic patients demonstrate that multiway analysis of an Epilepsy Feature Tensor can detect (patient-specific) seizures with classification accuracy ranging between 77-96%.

Published
2007
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