Eike Petersen, M. Sc.

Research Associate


Institute for Electrical Engineering in Medicine
Universität zu Lübeck
Moislinger Allee 53-55
23558 Lübeck
Gebäude 19,

Email:eike.petersen(at)uni-luebeck.de
Phone:+49 451 3101 6212
Fax:

 

I am currently working towards my Ph.D. at the intersection of mathematical modeling, signal processing, parameter identification & statistical inference, all driven by several biomedical applications. Most of my projects are related to surface electromyography (EMG), respiration, or both.

Research

Research Interests

  • Mathematical modeling and analysis of dynamical systems
  • Bioelectricity and biomechanics
  • Robust parameter and state estimation in physiological systems
  • Probabilistic inference algorithms using graphical models
  • Biomedical signal processing

Current Projects

  • Surface electromyography (EMG) for the identification of patient physiology
  • Control of medical assistive devices based on estimated patient activity
  • Probabilistic approaches to estimation and filtering problems

 

Resources


 

Curriculum Vitae

I hold a B.Sc. degree in Computer Science and Engineering from Hamburg University of Technology, Germany, since 2012. In 2015, I received my M.Sc. degree in Industrial Mathematics from Hamburg University, having spent one semester at Institut National des Sciences Appliquées, Toulouse, France. During the entire term of my studies, I was with Dräger Medical GmbH, working on various scientific problems related to mechanical ventilation.


Publications

Journal Publications

2019

  • Herzog, C., Petersen, E. and Rostalski, P.: Iterative Approximate Nonlinear Inference via Gaussian Message Passing on Factor Graphs IEEE Contr. Syst. Letters, vol. 3, no. 4, 2019
    BibTeX Link
    @article{HePeRo19,
    author = {Herzog, Christian and Petersen, Eike and Rostalski, Philipp},
    year = {2019},
    journal = {IEEE Contr. Syst. Letters},
    volume = {3},
    number = {4},
    title = {{Iterative Approximate Nonlinear Inference via Gaussian Message Passing on Factor Graphs}},
    doi = {https://dx.doi.org/10.1109/LCSYS.2019.2919260}
    }
    
  • Petersen, E. and Rostalski, P.: A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography and Force Generation frontiers in Physiology, 2019
    BibTeX Link
    @article{PeRo19,
     author = {Petersen, Eike and Rostalski, Philipp},
     abstract = {},
     year = {2019},
     title = {{A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography and Force Generation}},
     journal = {frontiers in Physiology},
     url = {{https://www.frontiersin.org/articles/10.3389/fphys.2019.00176/abstract}}
    }
    
    
    

2017

  • Petersen, E., Buchner, H., Eger, M. and Rostalski, P.: Convolutive blind source separation of surface EMG measurements of the respiratory muscles Biomed. Tech., vol. 62, no. 2, pp. 171-181, 2017
    BibTeX
    @article{PeBuEgRo17,
     author = {Petersen, Eike and Buchner, Herbert and Eger, Marcus and Rostalski, Philipp},
     abstract = {Electromyography (EMG) has long been used for the assessment of muscle function and activity and has recently been applied to the control of medical ventilation. For this application, the EMG signal is usually recorded invasively by means of electrodes on a nasogastric tube which is placed inside the esophagus in order to minimize noise and crosstalk from other muscles. Replacing these invasive measurements with an EMG signal obtained non-invasively on the body surface is difficult and requires techniques for signal separation in order to reconstruct the contributions of the individual respiratory muscles. In the case of muscles with small cross-sectional areas, or with muscles at large distances from the recording site, solutions to this problem have been proposed previously. The respiratory muscles, however, are large and distributed widely over the upper body volume. In this article, we describe an algorithm for convolutive blind source separation (BSS) that performs well even for large, distributed muscles such as the respiratory muscles, while using only a small number of electrodes. The algorithm is derived as a special case of the TRINICON general framework for BSS. To provide evidence that it shows potential for separating inspiratory, expiratory, and cardiac activities in practical applications, a joint numerical simulation of EMG and ECG activities was performed, and separation success was evaluated in a variety of noise settings. The results are promising.
    
    ~
    
    Electromyography (EMG) has long been used for the assessment of muscle function and activity and has recently been applied to the control of medical ventilation. For this application, the EMG signal is usually recorded invasively by means of electrodes on a nasogastric tube which is placed inside the esophagus in order to minimize noise and crosstalk from other muscles. Replacing these invasive measurements with an EMG signal obtained non-invasively on the body surface is difficult and requires techniques for signal separation in order to reconstruct the contributions of the individual respiratory muscles. In the case of muscles with small cross-sectional areas, or with muscles at large distances from the recording site, solutions to this problem have been proposed previously. The respiratory muscles, however, are large and distributed widely over the upper body volume. In this article, we describe an algorithm for convolutive blind source separation (BSS) that performs well even for large, distributed muscles such as the respiratory muscles, while using only a small number of electrodes. The algorithm is derived as a special case of the TRINICON general framework for BSS. To provide evidence that it shows potential for separating inspiratory, expiratory, and cardiac activities in practical applications, a joint numerical simulation of EMG and ECG activities was performed, and separation success was evaluated in a variety of noise settings. The results are promising.
    
    // 
    
    Electromyography (EMG) has long been used for the assessment of muscle function and activity and has recently been applied to the control of medical ventilation. For this application, the EMG signal is usually recorded invasively by means of electrodes on a nasogastric tube which is placed inside the esophagus in order to minimize noise and crosstalk from other muscles. Replacing these invasive measurements with an EMG signal obtained non-invasively on the body surface is difficult and requires techniques for signal separation in order to reconstruct the contributions of the individual respiratory muscles. In the case of muscles with small cross-sectional areas, or with muscles at large distances from the recording site, solutions to this problem have been proposed previously. The respiratory muscles, however, are large and distributed widely over the upper body volume. In this article, we describe an algorithm for convolutive blind source separation (BSS) that performs well even for large, distributed muscles such as the respiratory muscles, while using only a small number of electrodes. The algorithm is derived as a special case of the TRINICON general framework for BSS. To provide evidence that it shows potential for separating inspiratory, expiratory, and cardiac activities in practical applications, a joint numerical simulation of EMG and ECG activities was performed, and separation success was evaluated in a variety of noise settings. The results are promising.},
     year = {2017},
     title = {{Convolutive blind source separation of surface EMG measurements of the respiratory muscles}},
     pages = {171--181},
     volume = {62},
     number = {2},
     journal = {Biomed. Tech.},
     note = {Evaluation Studies
    
    Journal Article}
    }
    
    
    

Conference Publications

2018

  • Petersen, E. and Rostalski, P.: Static Nonlinear Transformation of Excitation Model Input as an Alternative to Feedback Control in EMG-Force Models in XXII Congr. Intl. Soc. Electrophysiology and Kinesiology (ISEK), Dublin, 2018
    BibTeX
    @inproceedings{PeRo18e,
    address = {Dublin},
    year = {2018},
    author = {Petersen, Eike and Rostalski, Philipp},
    booktitle = {XXII Congr. Intl. Soc. Electrophysiology and Kinesiology (ISEK)},
    title = {{Static Nonlinear Transformation of Excitation Model Input as an Alternative to Feedback Control in EMG-Force Models}},
    }
    
  • Olbrich, M., Petersen, E., Hoffmann, C. and Rostalski, P.: Sparse Estimation for the Assessment of Muscular Activity based on sEMG Measurements in Proc. 18th Symp. Syst. Ident., 2018
    BibTeX
    @inproceedings{OlPeHoRo18,
     author = {Olbrich, Michael and Petersen, Eike and Hoffmann, Christian and Rostalski, Philipp},
     abstract = {},
     title = {{Sparse Estimation for the Assessment of Muscular Activity based on sEMG Measurements}},
     year = {2018},
     booktitle = {Proc. 18th Symp. Syst. Ident.}
    }
    
    
    
  • Petersen, E., Hoffmann, C. and Rostalski, P.: On Approximate Nonlinear Gaussian Message Passing on Factor Graphs in Proc. Stat. Sig. Proc. Workshop, 2018
    BibTeX
    @inproceedings{PeHoRo18,
     author = {Petersen, Eike and Hoffmann, Christian and Rostalski, Philipp},
     abstract = {},
     title = {{On Approximate Nonlinear Gaussian Message Passing on Factor Graphs}},
     year = {2018},
     booktitle = {Proc. Stat. Sig. Proc. Workshop}
    }
    
    
    
  • Petersen, E. and Rostalski, P.: Mathematical Analysis of a Model of Intracellular Action Potential Generation in XXII Congr. Intl. Soc. Electrophysiology and Kinesiology (ISEK), Dublin, 2018
    BibTeX
    @inproceedings{PeRo18d,
    address = {Dublin},
    year = {2018},
    author = {Petersen, Eike and Rostalski, Philipp},
    booktitle = {XXII Congr. Intl. Soc.  Electrophysiology and Kinesiology (ISEK)},
    title = {{Mathematical Analysis of a Model of Intracellular Action Potential Generation}},
    }
    
  • Petersen, E., Kahl, L. and Rostalski, P.: Electromyography as a tool for personalized rehabilitation in 52nd Ann. Conf. Ger. Soc. Biomed. Eng. (DGBMT within VDE), Aachen, 2018
    BibTeX
    @inproceedings{PeKaRo18,
    address = {Aachen},
    year = {2018},
    author = {Petersen, Eike and Kahl, Lorenz and Rostalski, Philipp},
    booktitle = {52nd Ann. Conf. Ger. Soc. Biomed. Eng. (DGBMT within VDE)},
    title = {{Electromyography as a tool for personalized rehabilitation}}
    }
    

2017

  • Graßhoff, J., Petersen, E., Eger, M., Bellani, G. and Rostalski, P.: A Template Subtraction Method for the Removal of Cardiogenic Oscillations on Esophageal Pressure Signals in roc. 39th Ann. Int. Conf. Eng. Med. Biol. Soc., 2017
    BibTeX
    @inproceedings{GrPeEgBe17,
     author = {Graßhoff, Jan and Petersen, Eike and Eger, Marcus and Bellani, Giacomo and Rostalski, Philipp},
     abstract = {Esophageal pressure (Pes) is usually measured in patients receiving mechanical ventilation and is used for the assessment of lung mechanics. However, its interpretation is complicated by the presence of cardiogenic oscillations (CGO). In this article we present a novel method for the reduction of CGO based on the identification of pressure templates. Similar approaches are known for the removal of electrocardiographic (ECG) artifacts from the electromyogram (EMG). The proposed method is tested on clinical recordings of patients under assisted spontaneous ventilation. Besides the improvement of the respiratory signals, the identified CGO templates can be used diagnostically when viewed in relation to corresponding ECG data. This approach is illustrated on a few sample datasets.},
     title = {{A Template Subtraction Method for the Removal of Cardiogenic Oscillations on Esophageal Pressure Signals}},
     year = {2017},
     booktitle = {Proc. 39th Ann. Int. Conf. Eng. Med. Biol. Soc.}
    }
    
    
    
  • Hoffmann, C., Petersen, E., Handzsuj, T., Bellani, G. and Rostalski, P.: A Factor Graph-Based Change Point Detection Algorithm With an Application to sEMG-Onset and Activity Detection in Ann. Conf. German Soc. Biomed. Eng., vol. 62, pp. 116-120, Biomedical Engineering / Biomedizinische Technik, Abstract and Poster in Session 6. Biosignal Processing and Monitoring I, 2017
    BibTeX
    @inproceedings{HoPeHaBe17,
     author = {Hoffmann, Christian and Petersen, Eike and Handzsuj, Thomas and Bellani, Giacomo and Rostalski, Philipp},
     abstract = {},
     title = {{A Factor Graph-Based Change Point Detection Algorithm With an Application to sEMG-Onset and Activity Detection}},
     pages = {116--120},
     volume = {62},
     series = {Biomedical Engineering / Biomedizinische Technik, Abstract and Poster in Session 6. Biosignal Processing and Monitoring I},
     year = {2017},
     booktitle = {Ann. Conf. German Soc. Biomed. Eng.}
    }
    
    
    

2016

  • Buchner, H., Petersen, E., Eger, M. and Rostalski, P.: Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control in Proc. 38th Ann. Int. Conf. Eng. Med. Biol. Soc, vol. 2016, pp. 3626-3629, 2016
    BibTeX
    @inproceedings{BuPeEgRo16,
     author = {Buchner, Herbert and Petersen, Eike and Eger, Marcus and Rostalski, Philipp},
     abstract = {The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an effective tool to pre-process surface electromyogram (sEMG) data of the human respiratory muscles. Specifically, the problem of discriminating between inspiratory, expiratory and cardiac muscle activity is addressed, which currently poses a major obstacle for the clinical use of sEMG for adaptive ventilation control. It is shown that using the investigated broadband algorithm, a clear separation of these components can be achieved. The algorithm is based on a generic framework for BSS that utilizes multiple statistical signal characteristics. Apart from a four-channel FIR structure, there are no further restrictive assumptions on the demixing system.},
     title = {{Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control}},
     pages = {3626--3629},
     volume = {2016},
     year = {2016},
     booktitle = {Proc. 38th Ann. Int. Conf. Eng. Med. Biol. Soc}
    }
    
    
    
  • Petersen, E.: A Mathematical Model of Surface Electromyographic Measurements in Proc. Workshop Biosig. Process, 2016
    BibTeX
    @inproceedings{Pe16,
     author = {Petersen, Eike},
     abstract = {},
     title = {{A Mathematical Model of Surface Electromyographic Measurements}},
     year = {2016},
     booktitle = {Proc. Workshop Biosig. Process}
    }
    
    
    

Theses

2015

  • Petersen, E.: Integrative Mathematical Modelling and Simulation of Surface Electromyographic Measurements Hamburg, Germany, 2015
    BibTeX
    @phdthesis{Pe15,
     author = {Petersen, Eike},
     abstract = {},
     year = {2015},
     title = {{Integrative Mathematical Modelling and Simulation of Surface Electromyographic Measurements}},
     address = {Hamburg, Germany},
     school = {{University of Hamburg}},
     type = {{Master's Thesis}}
    }
    
    
    

2012

  • Petersen, E.: Patient Airway Pressure Determination in Medical Ventilators using Transmission Line Theory Hamburg, Germany, 2012
    BibTeX
    @phdthesis{Pe12,
     author = {Petersen, Eike},
     abstract = {},
     year = {2012},
     title = {{Patient Airway Pressure Determination in Medical Ventilators using Transmission Line Theory}},
     address = {Hamburg, Germany},
     school = {{Hamburg University of Technology}},
     type = {{Bachelor Thesis}}
    }