Mitarbeiter

M. Sc. Sven Schellenberger

Kontakt

  • E-Mail:
  • Telefon: 09131/85-67733
  • Fax: Neu:09131/85-28730
  • Raum: 01.178 G
  • Neu: Wetterkreuz 15
    91058 Erlangen

Über Sven Schellenberger

Lebenslauf

Sven Schellenberger schloss sein Bachelorstudium im Fach Elektrotechnik-Elektronik-Informationstechnik an der Universität Erlangen-Nürnberg im April 2015 ab. Das anschließende Masterstudium Elektrotechnik-Elektronik-Informationstechnik an der Universität Erlangen-Nürnberg beendete er erfolgreich im Oktober 2017 mit Auszeichnung.

Arbeitsgebiete

  • Digitale Signalverarbeitung
  • Entwurf von Algorithmen zur Vitalparameterdetektion
  • Messung, Auswertung und Analyse von Vitalparametern

Abschlussarbeiten

Bitte melden, falls Interesse an einem der genannten Arbeitsgebiete besteht.

Preise & Auszeichnungen

  • K. Shi and S. Schellenberger, 3rd Prize Most Innovative Project Award, Innovation Research Lab Exhibition (IRLE), 2015. [Bibtex]
    @prize{shi_prize_2015,
    abstract = {Awarded Project: Contactless and Android-based real time heartbeat detection using a microwave radar},
    author = {Shi, Kilin and Schellenberger, Sven},
    booktitle = {Innovation Research Lab Exhibition (IRLE)},
    cris = {shi_prize_2015},
    year = {2015},
    month = {09},
    day = {03},
    title = {3rd Prize Most Innovative Project Award},
    type = {20773-Kleiner Preis},
    }

COPYRIGHT NOTICE: Copyright and all rights of the material above are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by the appropriate copyright. The material may not be reposted without the explicit permission of the copyright holder.

COPYRIGHT NOTICE FOR IEEE PUBLICATIONS: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

COPYRIGHT NOTICE FOR EUMA PUBLICATIONS: © EUMA. Personal use of this material is permitted. Permission from European Microwave Association(EUMA) must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publikationen

2019

  • K. Shi, S. Schellenberger, L. Weber, P. Wiedemann, F. Michler, T. Steigleder, A. Malessa, F. Lurz, C. Ostgathe, R. Weigel, and A. Koelpin, "Segmentation of Radar-Recorded Heart Sound Signals Using Bidirectional LSTM Networks" in 41st IEEE International Engineering in Medicine and Biology Conference, Berlin, Germany, 2019 (to be published). [Bibtex]
    @inproceedings{shi2019,
    abstract = {Sounds caused by the action of the heart reflect both its health as well as deficiencies and are examined by physicians since antiquity. Pathologies of the valves, e.g. insufficiencies and stenosis, cardiac effusion, arrhythmia, inflammation of the surrounding tissue and other diagnosis can be reached by experienced physicians. However, practice is needed to assess the findings correctly. Furthermore, stethoscopes do not allow for long-term monitoring of a patient. Recently, radar technology has shown the ability to perform continuous touchless and thereby burden-free heart sound measurements. In order to perform automated classification of the signals, the first and most important step is to segment the heart sounds into their physiological phases. This paper examines the use of different Long Short-Term Memory (LSTM) architectures for this purpose based on a large dataset of radar-recorded heart sounds gathered from 30 different test persons in a clinical study. The best-performing network, a bidirectional LSTM, achieves a sample-wise accuracy of 93.4% and a F1 score for the first heart sound of 95.8%.
    }, author = {Shi, Kilin and Schellenberger, Sven and Weber, Leon and Wiedemann, Philipp and Michler, Fabian and Steigleder, Tobias and Malessa, Anke and Lurz, Fabian and Ostgathe, Christoph and Weigel, Robert and Koelpin, Alexander}, language = {English}, booktitle = {41st IEEE International Engineering in Medicine and Biology Conference}, cris = {https://cris.fau.de/converis/publicweb/publication/215822199}, year = {2019}, month = {09}, day = {23}, eventdate = {2019-07-23/2019-07-27}, faupublication = {yes}, note = {unpublished}, peerreviewed = {Yes}, title = {Segmentation of Radar-Recorded Heart Sound Signals Using Bidirectional LSTM Networks}, type = {Konferenzschrift}, venue = {Berlin, Germany}, }
  • K. Shi, S. Schellenberger, F. Michler, T. Steigleder, A. Malessa, F. Lurz, C. Ostgathe, R. Weigel, and A. Koelpin, "Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification", IEEE Transactions on Biomedical Engineering, 2019. [Bibtex]
    @article{shi2019a,
    abstract = {Objective: Radar technology promises to be a
    touchless and thereby burden-free method for continuous
    heart sound monitoring which can be used to detect cardiovascular
    diseases. However, the first and most crucial step
    is to differentiate between high- and low-quality segments
    in a recording to assess their suitability for a subsequent
    automated analysis. This paper gives a comprehensive
    study on this task and firstly addresses the specific characteristics
    of radar-recorded heart sound signals. Methods:
    To gather heart sound signals recorded from radar, a
    bistatic radar system was built and installed at the university
    hospital. Under medical supervision, heart sound data
    were recorded from 30 healthy test subjects. The signals
    were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification
    algorithms were evaluated for the task of automated
    signal quality determination and the most promising one
    was optimized and evaluated using leave-one-subject-out cross-validation. Results: The proposed classifier is able to
    achieve an accuracy of up to 96.36% and demonstrates a
    superior classification performance compared to the stateof-
    the-art classifier with a maximum accuracy of 76.00 %.
    Conclusion: This paper introduces an ensemble classifier
    that is able to perform automated signal quality determination
    of radar-recorded heart sound signals with a
    high accuracy. Significance: Besides achieving a higher
    performance compared to state-of-the-art classifiers, the
    presented study is the first one to deal with the quality
    determination of heart sounds that are recorded by radar
    systems. The proposed method enables contactless and
    continuous heart sound monitoring for the detection of
    cardiovascular diseases.
    }, author = {Shi, Kilin and Schellenberger, Sven and Michler, Fabian and Steigleder, Tobias and Malessa, Anke and Lurz, Fabian and Ostgathe, Christoph and Weigel, Robert and Koelpin, Alexander}, cris = {https://cris.fau.de/converis/publicweb/publication/219338194}, year = {2019}, month = {08}, faupublication = {yes}, issn = {0018-9294}, journaltitle = {IEEE Transactions on Biomedical Engineering}, keywords = {biomedical engineering; biomedical informatics; biomedical signal processing; heart sounds; medical radar,pattern recognition; phonocardiography}, peerreviewed = {Yes}, shortjournal = {IEEE T BIO-MED ENG}, title = {Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification}, type = {Article in Journal}, }
  • F. Michler, K. Shi, S. Schellenberger, T. Steigleder, A. Malessa, L. Hameyer, N. Neumann, F. Lurz, C. Ostgathe, R. Weigel, and A. Koelpin, "A Clinically Evaluated Interferometric Continuous-Wave Radar System for the Contactless Measurement of Human Vital Parameters", Sensors, vol. 19, iss. 11, 2019. [DOI] [Bibtex]
    @article{michler2019c,
    abstract = {
    Vital parameters are key indicators for the assessment of health. Conventional methods rely on direct contact with the patients’ skin and can hence cause discomfort and reduce autonomy. This article presents a bistatic 24 GHz radar system based on an interferometric six-port architecture and features a precision of 1 µm in distance measurements. Placed at a distance of 40 cm in front of the human chest, it detects vibrations containing respiratory movements, pulse waves and heart sounds. For the extraction of the respiration rate, time-domain approaches like autocorrelation, peaksearch and zero crossing rate are compared to the Fourier transform, while template matching and a hidden semi-Markov model are utilized for the detection of the heart rate from sphygmograms and heart sounds. A medical study with 30 healthy volunteers was conducted to collect 5.5 h of data, where impedance cardiogram and electrocardiogram were used as gold standard for synchronously recording respiration and heart rate, respectively. A low root mean square error for the breathing rate (0.828 BrPM) and a high overall F1 score for heartbeat detection (93.14%) could be achieved using the proposed radar system and signal processing.
    }, author = {Michler, Fabian and Shi, Kilin and Schellenberger, Sven and Steigleder, Tobias and Malessa, Anke and Hameyer, Laura and Neumann, Nina and Lurz, Fabian and Ostgathe, Christoph and Weigel, Robert and Koelpin, Alexander}, language = {English}, cris = {https://cris.fau.de/converis/publicweb/publication/216337023}, year = {2019}, month = {05}, day = {31}, doi = {10.3390/S19112492}, faupublication = {yes}, issn = {1424-8220}, journaltitle = {Sensors}, number = {11}, peerreviewed = {Yes}, shortjournal = {SENSORS-BASEL}, title = {A Clinically Evaluated Interferometric Continuous-Wave Radar System for the Contactless Measurement of Human Vital Parameters}, type = {online publication}, url = {https://www.mdpi.com/1424-8220/19/11/2492}, volume = {19}, }
  • F. Michler, K. Shi, S. Schellenberger, B. Scheiner, F. Lurz, R. Weigel, and A. Koelpin, "Pulse Wave Velocity Detection Using a 24 GHz Six-Port Based Doppler Radar" in IEEE Radio and Wireless Symposium (RWS), Orlando, FL, USA, 2019, pp. 1-3. [DOI] [Bibtex]
    @inproceedings{michler2019a,
    author = {Michler, Fabian and Shi, Kilin and Schellenberger, Sven and Scheiner, Benedict and Lurz, Fabian and Weigel, Robert and Koelpin, Alexander},
    language = {English},
    booktitle = {IEEE Radio and Wireless Symposium (RWS)},
    cris = {https://cris.fau.de/converis/publicweb/publication/202125433},
    year = {2019},
    month = {01},
    day = {20},
    doi = {10.1109/RWS.2019.8714521},
    eventdate = {2019-01-20/2019-01-23},
    faupublication = {yes},
    isbn = {9781538659441},
    issn = {2164-2974},
    pages = {1--3},
    peerreviewed = {Yes},
    title = {Pulse Wave Velocity Detection Using a 24 GHz Six-Port Based Doppler Radar},
    type = {Konferenzschrift},
    venue = {Orlando, FL, USA},
    }
  • B. Scheiner, S. Schellenberger, K. Shi, E. Heusinger, F. Michler, F. Lurz, R. Weigel, and A. Koelpin, "Low-power contactless LC-tank based respiratory sensor", Electronics Letters, pp. 304-306, 2019. [DOI] [Bibtex]
    @article{scheiner2019a,
    author = {Scheiner, Benedict and Schellenberger, Sven and Shi, Kilin and Heusinger, Elisabeth and Michler, Fabian and Lurz, Fabian and Weigel, Robert and Koelpin, Alexander},
    cris = {https://cris.fau.de/converis/publicweb/publication/208613711},
    year = {2019},
    month = {01},
    day = {29},
    doi = {10.1049/EL.2018.7936},
    faupublication = {yes},
    issn = {0013-5194},
    journaltitle = {Electronics Letters},
    pages = {304--306},
    peerreviewed = {Yes},
    shortjournal = {ELECTRON LETT},
    title = {Low-power contactless LC-tank based respiratory sensor},
    type = {Letter},
    }

2018

  • K. Shi, S. Schellenberger, T. Steigleder, F. Michler, F. Lurz, R. Weigel, and A. Koelpin, "Contactless Carotid Pulse Measurement Using Continuous Wave Radar" in 2018 Asia-Pacific Microwave Conference, Kyoto, Japan, 2018. [Bibtex]
    @inproceedings{shi2018b,
    abstract = {Cardiovascular diseases are one of the major causes of death. Regular checkups and preventive actions can drastically help reducing fatal incidences. This can be achieved by monitoring the carotid artery or rather the carotid pulse signal. Commonly, ultrasound devices are used for that purpose. However, these devices are costly, mostly stationary and their usage requires training and experience. This paper investigates the possible usage of radar systems as a contactless and low-cost alternative for carotid pulse measurements. Theoretical investigations reveal a linear relationship between the measurands of both devices and synchronous recordings from three test persons further confirm the feasibility of using radar systems as a potential device for monitoring cardiovascular diseases.},
    author = {Shi, Kilin and Schellenberger, Sven and Steigleder, Tobias and Michler, Fabian and Lurz, Fabian and Weigel, Robert and Koelpin, Alexander},
    language = {English},
    booktitle = {2018 Asia-Pacific Microwave Conference},
    cris = {https://cris.fau.de/converis/publicweb/publication/202749417},
    year = {2018},
    month = {11},
    day = {06},
    eventdate = {2018-11-06/2018-08-09},
    faupublication = {yes},
    peerreviewed = {unknown},
    title = {Contactless Carotid Pulse Measurement Using Continuous Wave Radar},
    type = {Konferenzschrift},
    venue = {Kyoto, Japan},
    }
  • S. Schellenberger, K. Shi, M. Mai, J. P. Wiedemann, T. Steigleder, B. Eskofier, R. Weigel, and A. Koelpin, "Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks" in Computing in Cardiology, MECC Maastricht, Netherlands, 2018. [Bibtex]
    @inproceedings{schellenberger2018,
    author = {Schellenberger, Sven and Shi, Kilin and Mai, Melanie and Wiedemann, Jan Philipp and Steigleder, Tobias and Eskofier, Björn and Weigel, Robert and Koelpin, Alexander},
    language = {English},
    booktitle = {Computing in Cardiology},
    cris = {https://cris.fau.de/converis/publicweb/publication/202377694},
    year = {2018},
    month = {10},
    day = {23},
    eventdate = {2018-09-23/2018-09-26},
    faupublication = {yes},
    peerreviewed = {Yes},
    title = {Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks},
    type = {Konferenzschrift},
    venue = {MECC Maastricht, Netherlands},
    }
  • T. Steigleder, A. Malessa, K. Shi, F. Michler, S. Schellenberger, M. Heckel, A. Koelpin, and C. Ostgathe, "Kontinuierliche berührungslose Erfassung von Herzschlag und Atmung als Surrogatparameter für Symptomlinderung–eine Pilotstudie", Zeitschrift für Palliativmedizin, vol. 19, iss. 05, 2018. [DOI] [Bibtex]
    @article{steigleder2018,
    abstract = {Fokus der Palliativmedizin (PM) ist die persönliche Begegnung. Häufig wird bei schwerer Krankheit auf apparative Therapie und Diagnostik verzichtet. Dennoch könnten Biomarker (BM, zB Herz-und Atemfrequenz) wichtige ergänzende Hinweise auf Symptomlast und zur individuellen Anpassung der medikamentösen Behandlung geben. Wir erforschen den innovativen Ansatz, BM mit Radartechnologie (RT) berührungs-und belastungsfrei zu erfassen. Ziel soll es ua in sein, in Zukunft die Symptomlinderung zu verbessern. RT, die auf einem interferometrischem Verfahren beruht, erfasst Herzschläge und Atmung mittels Messung der Distanzänderung zu der Radarantenne aus einigen Metern Entfernung und durch Materialen wie Kleidung oder Bettdecke hindurch. Lernende Algorithmen extrahieren die spezifischen Signale und werten sie automatisiert aus.},
    author = {Steigleder, Tobias and Malessa, Anke and Shi, Kilin and Michler, Fabian and Schellenberger, Sven and Heckel, Maria and Koelpin, Alexander and Ostgathe, Christoph},
    language = {German},
    cris = {https://cris.fau.de/converis/publicweb/publication/203858370},
    year = {2018},
    month = {08},
    doi = {10.1055/S-0038-1669350},
    faupublication = {yes},
    issn = {1615-2921},
    journaltitle = {Zeitschrift für Palliativmedizin},
    number = {05},
    peerreviewed = {No},
    title = {Kontinuierliche berührungslose Erfassung von Herzschlag und Atmung als Surrogatparameter für Symptomlinderung–eine Pilotstudie},
    type = {Article in Journal},
    url = {https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0038-1669350},
    volume = {19},
    }
  • C. Will, K. Shi, S. Schellenberger, T. Steigleder, F. Michler, J. Fuchs, R. Weigel, C. Ostgathe, and A. Koelpin, "Radar-Based Heart Sound Detection", Scientific Reports, 2018. [DOI] [Bibtex]
    @article{will2018,
    abstract = {This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests' vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.
    }, author = {Will, Christoph and Shi, Kilin and Schellenberger, Sven and Steigleder, Tobias and Michler, Fabian and Fuchs, Jonas and Weigel, Robert and Ostgathe, Christoph and Koelpin, Alexander}, cris = {https://cris.fau.de/converis/publicweb/publication/202373734}, year = {2018}, month = {07}, day = {26}, doi = {10.1038/S41598-018-29984-5}, faupublication = {yes}, issn = {2045-2322}, journaltitle = {Scientific Reports}, peerreviewed = {Yes}, title = {Radar-Based Heart Sound Detection}, type = {online publication}, url = {https://www.nature.com/articles/s41598-018-29984-5}, }
  • S. Schellenberger, K. Shi, T. Steigleder, F. Michler, F. Lurz, R. Weigel, and A. Koelpin, "Support Vector Machine-Based Instantaneous Presence Detection for Continuous Wave Radar Systems" in 2018 Asia-Pacific Microwave Conference, Kyoto, Japan, 2018, pp. 1465-1467. [DOI] [Bibtex]
    @inproceedings{schellenberger2018a,
    abstract = {Instantaneous detection of missing vital signs at inpatient beds enables fast intervention for cardiac arrests.
    Using a 24 GHz bistatic radar, a fast presence detection based on a support vector machine (SVM) classifer is realized. Large body motions or even small distance deviations, such as movement of the chest induced by heartbeat or breathing, are distinguishable from the measured noise of an unoccupied bed. For classifcation two features are calculated based on windowed I and Q data. Performance is evaluated by varying window sizes from 0.2 ... 1.5 s for feature calculation and training of the SVM classifer. In the resting scenario an accuracy of 99.2% and F1-score of 99.1% with windows of 0.2 s is achieved.
    }, author = {Schellenberger, Sven and Shi, Kilin and Steigleder, Tobias and Michler, Fabian and Lurz, Fabian and Weigel, Robert and Koelpin, Alexander}, language = {English}, booktitle = {2018 Asia-Pacific Microwave Conference}, cris = {https://cris.fau.de/converis/publicweb/publication/203101192}, year = {2018}, month = {01}, day = {06}, doi = {10.23919/APMC.2018.8617181}, eventdate = {2018-11-06/2018-11-09}, faupublication = {yes}, pages = {1465--1467}, peerreviewed = {Yes}, title = {Support Vector Machine-Based Instantaneous Presence Detection for Continuous Wave Radar Systems}, type = {Journal Article}, venue = {Kyoto, Japan}, }

2017

  • C. Will, K. Shi, S. Schellenberger, T. Steigleder, F. Michler, R. Weigel, C. Ostgathe, and A. Koelpin, "Local Pulse Wave Detection using Continuous Wave Radar Systems", IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, vol. 1, iss. 2, pp. 81-89, 2017. [DOI] [Bibtex]
    @article{will2017e,
    author = {Will, Christoph and Shi, Kilin and Schellenberger, Sven and Steigleder, Tobias and Michler, Fabian and Weigel, Robert and Ostgathe, Christoph and Koelpin, Alexander},
    publisher = {IEEE},
    booktitle = {IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology},
    cris = {https://cris.fau.de/converis/publicweb/publication/123402224},
    year = {2017},
    month = {10},
    day = {27},
    doi = {10.1109/JERM.2017.2766567},
    faupublication = {yes},
    issn = {2469-7249},
    journaltitle = {IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology},
    number = {2},
    pages = {81--89},
    peerreviewed = {Yes},
    title = {Local Pulse Wave Detection using Continuous Wave Radar Systems},
    type = {Article in Journal},
    volume = {1},
    }

COPYRIGHT NOTICE: Copyright and all rights of the material above are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by the appropriate copyright. The material may not be reposted without the explicit permission of the copyright holder.

COPYRIGHT NOTICE FOR IEEE PUBLICATIONS: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

COPYRIGHT NOTICE FOR EUMA PUBLICATIONS: © EUMA. Personal use of this material is permitted. Permission from European Microwave Association(EUMA) must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.