TY - RPRT A1 - Peter, Steffen T1 - Periodic Activities and Management Report Y1 - 2011 ER - TY - RPRT A1 - Peter, Steffen T1 - Specification of Secure Code update Protocol for WSAN Y1 - 2011 ER - TY - RPRT A1 - Peter, Steffen T1 - Sensor Network Related Requirements Y1 - 2011 ER - TY - RPRT A1 - Peter, Steffen T1 - Tools and Methods for a Unified Secure design Flow of Sensor Node Hardware Y1 - 2011 ER - TY - RPRT A1 - Schmidt, Jörn-Marc A1 - Kirschbaum, Mario A1 - Francillon, Auélien A1 - Sekar, Manu A1 - Vater, Frank A1 - Peter, Steffen T1 - Analysis of Attacks on Sensor Nodes Software and Hardware Y1 - 2011 UR - https://graz.pure.elsevier.com/de/publications/tampres-d12-analysis-of-attacks-on-sensor-nodes-software-and-hard/projects/?status=FINISHED ER - TY - CHAP A1 - Ortmann, Steffen A1 - Maaser, Michael A1 - Langendörfer, Peter T1 - High Level Definition of Event-based Applications for Pervasive Systems Y1 - 2012 ER - TY - CHAP A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - Social Networking and Privacy, a Contradiction? Y1 - 2012 ER - TY - GEN A1 - Ortmann, Steffen A1 - Langendörfer, Peter A1 - Kornemann, Stephan T1 - WiSec 2011 Demo: Demonstrating Self-Contained on-node Counter Measures for Various Jamming Attacks in WSN Y1 - 2011 ER - TY - GEN A1 - Peter, Steffen T1 - Monitoring and Control of a Drinking Water Pipeline - An Application of Secure Wireless Sensor and Actuator Network Y1 - 2011 ER - TY - GEN A1 - Basmer, Thomas A1 - Schomann, Henry A1 - Peter, Steffen T1 - Implementation Analysis of the IEEE 802.15.4 MAC for Wireless Sensor Networks Y1 - 2011 ER - TY - CHAP A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - A Telemedicine System for Improved Rehabilitation of Stroke Patients Y1 - 2011 ER - 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 -