Respiratory Monitoring & Control
Modeling, monitoring and control of respiratory support
Breathing is a process most of us take for granted, and its failure is a medical emergency. Providing personalized respiratory support is a huge challenge: Every patient has different metabolic needs, different respiratory anatomy and physiology, and a different natural breathing rhythm. Studies have shown that many patients are not ventilated in a way that supports their natural respiratory rhythm, potentially leading to grave consequences ranging from patient trauma due to a loss of autonomy over one’s own respiration over severe lung injury to death.
Our vision is a mechanical ventilator that…
- Monitors a patient’s own respiratory activity during mechanical ventilation,
- Identifies a model of the patient’s respiratory system, and
- Uses this information to optimally align ventilator support with the patient’s needs by using advanced control techniques.
More specifically, we…
- Apply advanced signal processing techniques to measurement modalities such as camera signals, respiratory surface EMG measurements and esophageal pressure measurements to obtain reliable measurements of respiratory activity.
- Model all aspects of the respiratory system, including respiratory mechanics and lung properties, gas exchange, and respiratory control mechanisms.
- Use modern control techniques such as model-predictive control (MPC) to design advanced ventilator control schemes based on these models and measurements.
2020
{On the Estimation of Optoacoustic Waves in Retinal Laser Therapy Using Gaussian Processes}, in Proc. Workshop Automed , Lübeck, Germany , 2020.
Registration of Image Modalities for Analyses of Tissue Samples using 3D Image Modelling, PROTEOMICS -- Clinical Applications , 2020. Wiley.
DOI: | {{10.1002/prca.201900143}} |
File: | prca.201900143}} |
Bibtex: | ![]() @article{HeBr20, title={Registration of Image Modalities for Analyses of Tissue Samples using 3D Image Modelling}, author={Hermann, Juliane and Brehmer, Kai and Mahfoud, Felix and Speer, Timotheus and Schunk, Stefan J. and Tscherning, Thomas and Thiele, Herbert and Jankowski, Joachim}, journal={PROTEOMICS -- Clinical Applications}, year={2020}, publisher={Wiley}, } |
Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms, {IEEE} Access , vol. 8, pp. 30905--30917, 2020. Institute of Electrical and Electronics Engineers ({IEEE}).
DOI: | 10.1109/access.2020.2972731 |
File: | 8988257 |
Bibtex: | ![]() @Article{PeSaGrRo20, author = {Petersen, Eike and Sauer, Julia and Graßhoff, Jan and Rostalski, Philipp}, title = {Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms}, journal = {{IEEE} Access}, year = {2020}, volume = {8}, pages = {30905--30917}, doi = {10.1109/access.2020.2972731}, groups = {ECG Removal from EMG recordings}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})} } |
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase, in Proceedings of the 37th International Conference on International Conference on Machine Learning (ICML) , 2020.
Spatio-Temporal Gaussian Processes for Separation of Ventilation and Perfusion Related Signals in EIT Data, in Proc. Workshop Automed , 2020.
Surface {EMG}-based Estimation of Breathing Effort for Neurally Adjusted Ventilation Control, in {Proceedings of the 21st IFAC World Congress} , 2020.
2019
Automatic Estimation of Respiratory Effort using Esophageal Pressure, in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , 2019. pp. 4646-4649.
DOI: | 10.1109/EMBC.2019.8856345 |
Bibtex: | ![]() @inproceedings{GrPeBeRo19, author={J. {Graßhoff} and E. {Petersen} and T. {Becher} and P. {Rostalski}}, booktitle={2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, title={Automatic Estimation of Respiratory Effort using Esophageal Pressure}, year={2019}, pages={4646-4649}, doi={10.1109/EMBC.2019.8856345}, ISSN={1557-170X}, month={July}} |
Deep Learning for Prediction of Diaphragm Activity from the Surface Electromyogram, in Directions in Biomedical Engineering , De Gruyter, 2019. pp. 17--20.
2018
{Sparse Estimation for the Assessment of Muscular Activity based on sEMG Measurements}, in Proc. 18th Symp. Syst. Ident. , 2018.
2017
{A Template Subtraction Method for the Removal of Cardiogenic Oscillations on Esophageal Pressure Signals}, in Proc. 39th Ann. Int. Conf. Eng. Med. Biol. Soc. , 2017.
{Convolutive blind source separation of surface EMG measurements of the respiratory muscles}, Biomed. Tech. , vol. 62, no. 2, pp. 171--181, 2017.
Members
Philipp Rostalski
Gebäude 19
philipp.rostalski(at)uni-luebeck.de
+49 451 3101 6200
Jan Graßhoff
Gebäude 19
j.grasshoff(at)uni-luebeck.de
+49 451 3101 6216
Georg Männel
Gebäude 19
ge.maennel(at)uni-luebeck.de
+49 451 3101 6214
Sandra Henn
Gebäude 19
s.henn(at)uni-luebeck.de
+49 451 3101 6225
Julia Sauer
Gebäude 19
j.sauer(at)uni-luebeck.de
+49 451 3101 6217
Carlotta Hennigs
Gebäude 19
carlotta.hennigs(at)uni-luebeck.de
+49 451 3101 6218
Franziska Bilda (née Schollemann)
Gebäude 19
franziska.schollemann(at)uni-luebeck.de
+49 451 3101 6234