@misc{OrtmannMaaserLangendoerfer, author = {Ortmann, Steffen and Maaser, Michael and Langend{\"o}rfer, Peter}, title = {Self-Adapting Event Configuration in Ubiquitous Wireless Sensor Networks}, language = {en} } @misc{OrtmannMaaserLangendoerfer, author = {Ortmann, Steffen and Maaser, Michael and Langend{\"o}rfer, Peter}, title = {Adaptive Pruning of Event Decision Trees for Energy Efficient Collaboration in Event Driven WSN}, language = {en} } @inproceedings{OrtmannLangendoerfer, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {Enhancing Reliability of Sensor Networks by Fine Tuning Their Event Observation Behavior}, language = {en} } @article{OrtmannLangendoerferMaaser, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter and Maaser, Michael}, title = {Adaptive Pervasive Systems to Multiuser Privacy Requirements}, language = {en} } @incollection{MaaserOrtmannLangendoerfer, author = {Maaser, Michael and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {The Privacy Advocate: Assertion of Privacy by Personalised Contracts}, language = {en} } @misc{MaaserOrtmannLangendoerfer, author = {Maaser, Michael and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {NEPP: Negotiation Enhancements for Privacy Policies}, language = {en} } @misc{OrtmannLangendoerferMaaser, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter and Maaser, Michael}, title = {A Self-Configuring Privacy Management Architecture for Pervasive Systems}, language = {en} } @inproceedings{MaaserOrtmannLangendoerfer, author = {Maaser, Michael and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {The Privacy Advocate (PrivAd): A Framework for Negotiating Individualized Privacy Contracts}, language = {en} } @misc{OrtmannLangendoerferMaaser, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter and Maaser, Michael}, title = {Enhancing Privacy by Applying Information Flow Modelling in Pervasive Systems}, language = {en} } @incollection{OrtmannMaaserLangendoerfer, author = {Ortmann, Steffen and Maaser, Michael and Langend{\"o}rfer, Peter}, title = {High Level Definition of Event-based Applications for Pervasive Systems}, language = {en} } @incollection{OrtmannLangendoerfer, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {Social Networking and Privacy, a Contradiction?}, language = {en} } @misc{OrtmannLangendoerferKornemann, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter and Kornemann, Stephan}, title = {WiSec 2011 Demo: Demonstrating Self-Contained on-node Counter Measures for Various Jamming Attacks in WSN}, language = {en} } @inproceedings{OrtmannLangendoerfer, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {A Telemedicine System for Improved Rehabilitation of Stroke Patients}, language = {en} } @inproceedings{OrtmannLangendoerfer, author = {Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {The Impact of Social Networks on User Privacy - What Social Networks Really Learn about their Users}, language = {en} } @misc{HaddadiEsfahaniDykaOrtmannetal., author = {Haddadi Esfahani, Ali and Dyka, Zoya and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {Impact of Data Preparation in Freezing of Gait Detection using Feature-Less Recurrent Neural Network}, series = {IEEE Access}, journal = {IEEE Access}, number = {9}, issn = {2169-3536}, doi = {10.1109/ACCESS.2021.3117543}, pages = {138120 -- 138131}, language = {en} } @misc{PidvalnyiKostenkoSudakovetal., author = {Pidvalnyi, Illia and Kostenko, Anna and Sudakov, Oleksandr and Isaev, Dmytro and Maximyuk, Oleksandr and Krishtal, Oleg and Iegorova, Olena and Kabin, Ievgen and Dyka, Zoya and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {Classification of epileptic seizures by simple machine learning techniques : application to animals' electroencephalography signals}, series = {IEEE access}, volume = {13}, journal = {IEEE access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3527866}, pages = {8951 -- 8962}, abstract = {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.}, language = {en} }