Title: Detecting Respiratory Artifacts from Video Data. <em>Bildverarbeitung für die Medizin 2015 Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck</em>
Written by: S.-T. Antoni and R. Plagge and R. Dürichen and A. Schlaefer
in: <em>Informatik aktuell</em>. February (2015).
Volume: Number:
on pages: 227-232
Chapter: Bildverarbeitung für die Medizin 2015
Editor: In Handels, Heinz and Deserno, Thomas Martin and Meinzer, Hans-Peter and Tolxdorff, Thomas (Eds.)
Publisher: Springer Berlin Heidelberg:
Series: Informatik aktuell
ISBN: 978-3-662-46223-2
how published:
DOI: 10.1007/978-3-662-46224-9_40
URL: http://link.springer.com/chapter/10.1007%2F978-3-662-46224-9_40

[www] [BibTex]


Abstract: Detecting artifacts in signals is an important problem in a wide number of research areas. In robotic radiotherapy motion prediction is used to overcome latencies in the setup, with robustness effected by the occurrence of artifacts. For motion prediction the detection and especially the definition of artifacts can be challenging. We study the detection of artifacts like, e.g., coughing, sneezing or yawning. Manual detection can be time consuming. To assist manual annotation, we introduce a method based on kernel density estimation to detect intervals of artifacts on video data. We evaluate our method on a small set of test subjects. With 86 intervals of artifacts found by our method we are able to identify all 70 intervals derived from manual detection. Our results indicate a more exact choice of intervals and the identification of subtle artifacts like swallowing, that where missed in the manual detection.

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