René Pallenberg, M.Sc.

Photo of René  Pallenberg, M.Sc.

Research Associate

Institute for Signal Processing
University of Luebeck
Ratzeburger Allee 160
23562 Lübeck

Gebäude 64, 1. OG, Raum 92

Email: r.pallenberg(at)
Phone: +49 451 3101 5820

Research Focus/ Forschungsschwerpunkt

Auditory Attention Detection:

Investigates the detection of a subject's attentional focus during listening with the help of EEG. The EEG signal is used to predict which speaker a subject is currently listening to or in which direction their audio focus lies. This could be used to improve hearing aids in the future. Within this research topic, both EEG and audio data are processed. In addition to classical signal processing and pattern recognition methods such as filter banks and SVMs, current artificial neural networks are also used.

Erkennung der auditiven Aufmerksamkeit:

Untersucht die Erkennung des Aufmerksamkeitsfokus einer Person während des Zuhörens mithilfe eines EEGs. Das EEG-Signal wird verwendet, um vorherzusagen, welchem Sprecher ein Proband gerade zuhört oder in welche Richtung sein Audio-Fokus liegt. Dies könnte in Zukunft zur Verbesserung von Hörgeräten genutzt werden. In diesem Forschungsthema werden sowohl EEG- als auch Audiodaten verarbeitet. Neben klassischen Signalverarbeitungs- und Mustererkennungsmethoden wie Filterbanken und SVMs kommen auch aktuelle künstliche neuronale Netze zum Einsatz.




% % This file was created by the TYPO3 extension % publications % --- Timezone: CEST % Creation date: 2024-06-22 % Creation time: 22:16:16 % --- Number of references % 5 % @Article { fleitmann_artificial_2024, author = {Fleitmann, Marja and Uzunova, Hristina and Pallenberg, Ren{\'e} and Stroth, Andreas M. and Gerlach, Jan and F{\{\dq}u}rschke, Alexander and Barkhausen, J{\{\dq}o}rg and Bischof, Arpad and Handels, Heinz}, title = {Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets}, abstract = {Objectives In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature. Methods This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification. Results For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN. Conclusion We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.}, year = {2024}, month = {jan}, issn = {0026-1270, 2511-705X}, DOI = {10.1055/s-0044-1778694}, journal = {Methods of Information in Medicine}, keywords = {clinical decision support system, contrast medium, CT angiography, deep learning, machine learning}, file_url = {}, note = {Publisher: Georg Thieme Verlag KG} } @Inproceedings { 10289779, author = {Pallenberg, Ren{\'e} and Griedelbach, Ann-Katrin and Mertins, Alfred}, title = {LSTMs for EEG-based Auditory Attention Decoding}, year = {2023}, DOI = {10.23919/EUSIPCO58844.2023.10289779}, booktitle = {2023 31st European Signal Processing Conference (EUSIPCO)}, pages = {1055-1059} } @Article { pallenberg_random_2023, author = {Pallenberg, Ren{\'e} and Fleitmann, Marja and Stroth, Andreas Martin and Gerlach, Jan and F{\{\dq}u}rschke, Alexander and Barkhausen, J{\{\dq}o}rg and Bischof, Arpad and Handels, Heinz}, title = {Random Forest and Gradient Boosted Trees for Patient Individualized Contrast Agent Dose Reduction in CT Angiography}, year = {2023}, DOI = {10.3233/SHTI230316}, journal = {CARING IS SHARING--EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION}, pages = {952}, file_url = { - - pallenberg_random_2023} } @Inproceedings { katzberg2022, author = {Katzberg, Fabrice and Maa{\{\dq}s}, Marco and Pallenberg, Ren{\'e} and Mertins, Alfred}, title = {Positional Tracking of a Moving Microphone in Reverberant Scenes by Applying Perfect Sequences to Distributed Loudspeakers}, year = {2022}, month = {September}, booktitle = {Proc. International Workshop on Acoustic Signal Enhancement (IWAENC)}, address = {Bamberg, Germany}, file_url = {fileadmin/files/publications/katzberg2022.pdf} } @Article { pallenberg_automatic_2020, author = {Pallenberg, Ren{\'e} and Fleitmann, Marja and Soika, Kira and Stroth, Andreas Martin and Gerlach, Jan and F{\{\dq}u}rschke, Alexander and Barkhausen, J{\{\dq}o}rg and Bischof, Arpad and Handels, Heinz}, title = {Automatic quality measurement of aortic contrast-enhanced CT angiographies for patient-specific dose optimization}, abstract = {Iodine-containing contrast agent ({CA}) used in contrast-enhanced {CT} angiography ({CTA}) can pose a health risk for patients. A system that adjusts the frequently used standard {CA} dose for individual patients based on their clinical parameters can be useful. As basis the quality of the image contrast in {CTA} volumes has to be determined, especially to recognize excessive contrast induced by {CA} overdosing. However, a manual assessment with a {ROI}-based image contrast classification is a time-consuming step in everyday clinical practice.}, year = {2020}, month = {October}, issn = {1861-6429}, DOI = {10.1007/s11548-020-02238-4}, volume = {15}, pages = {1611--1617}, number = {10}, file_url = {fileadmin/files/publications/Pallenberg2020_Article_AutomaticQualityMeasurementOfA.pdf} }