TY - CHAP A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - The Impact of Social Networks on User Privacy - What Social Networks Really Learn about their Users Y1 - 2011 ER - TY - GEN A1 - Haddadi Esfahani, Ali A1 - Dyka, Zoya A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - Impact of Data Preparation in Freezing of Gait Detection using Feature-Less Recurrent Neural Network T2 - IEEE Access Y1 - 2021 U6 - https://doi.org/10.1109/ACCESS.2021.3117543 SN - 2169-3536 IS - 9 SP - 138120 EP - 138131 ER - TY - GEN A1 - Pidvalnyi, Illia A1 - Kostenko, Anna A1 - Sudakov, Oleksandr A1 - Isaev, Dmytro A1 - Maximyuk, Oleksandr A1 - Krishtal, Oleg A1 - Iegorova, Olena A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - Classification of epileptic seizures by simple machine learning techniques : application to animals’ electroencephalography signals T2 - IEEE access N2 - Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve the quality of life for epilepsy patients while also open new opportunities for pharmacological intervention. This paper presents a novel approach using machine learning techniques to detect seizures onset using intracranial electroencephalography (EEG) signals. The proposed approach was tested on intracranial EEG data recorded in rats with pilocarpine model of temporal lobe epilepsy. A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth’s parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. We found that the support vector machine approach had a classification sensitivity of 90% and a specificity of 74% for detecting ictal episodes. Changing the epoch parameter from one to twenty-one seconds results in changing the redistribution of principal components’ values to 10% but does not affect the classification result. Support vector machines are accessible and convenient methods for classification that have achieved promising classification quality, and are rather lightweight compared to other machine learning methods. So we suggest their future use in mobile devices for early epileptic seizure and preictal episode detection. KW - Epilepsy KW - Single-channel intracranial encephalographic data KW - PCA KW - SVM KW - Automated system KW - Rats Y1 - 2025 U6 - https://doi.org/10.1109/ACCESS.2025.3527866 SN - 2169-3536 VL - 13 SP - 8951 EP - 8962 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway, NJ ER -