TY  - JOUR
A1  - Keutmann, Sabine
A1  - Staat, Manfred
A1  - Laack, Walter van
T1  - Untersuchung der thermischen Auswirkung von therapeutischem Ultraschall
N2  - Zusammenfassung: In der Orthopädie zählt der therapeutische Ultraschall als Mittel zur Prävention und Therapiebegleitung. Er hat mechanische, thermische und physiko-chemische Auswirkungen auf den menschlichen Körper. Um mehr Erkenntnisse über die thermischen Auswirkungen zu erlangen, wurden Versuche an einem Hydrogel-Phantom und an Probanden durchgeführt. Dabei entstand eine signifikante Erwärmung des Gewebes, welche beim Probandenversuch an der Oberfläche und beim Hydrogelversuch in der Tiefe gemessen wurde.

Summary: In orthopaedics, therapeutic ultrasound is a tool of prevention and therapy support. It has mechanical, thermal and physico-chemical effects on the human body. Tests with a hydrogel phantom and with human probands have been performed in order to obtain more knowledge about their thermal effects. Both tests measured temperature increases in cell tissue, on the surface with the human proband test and in depth with the hydrogel phantom test.
T2  - Research about the thermal effects of therapeutic ultrasound
Y1  - 2018
SN  - 2193-5793
SN  - 2193-5785 (Druckausgabe)
VL  - 7
IS  - 10
SP  - 518
EP  - 522
PB  - Deutscher Ärzte-Verl.
CY  - Köln
ER  - 
TY  - CHAP
A1  - Groß, Rolf Fritz
T1  - Möglichkeiten und Grenzen für Forschung an Fachhochschulen
T2  - Smart Building Convention und BIMconvention in Aachen im September
Y1  - 2018
N1  - 10. und 11. September 2018, Aachen
ER  - 
TY  - JOUR
A1  - Schwabedal, Justus T. C.
A1  - Sippel, Daniel
A1  - Brandt, Moritz D.
A1  - Bialonski, Stephan
T1  - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
N2  - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Y1  - 2018
U6  - http://dx.doi.org/10.48550/arXiv.1809.08443
ER  -