Detecting respiratory artifacts from video data

Start Page
227
End Page
232
Citation
Workshops on Image Processing for Medicine, 2015: 227-232
Contribution to Conference
Workshops on Image Processing for Medicine, 2015  
Publisher DOI
10.1007/978-3-662-46224-9_40
Scopus ID
2-s2.0-85012195628
Publisher
Springer Vieweg
ISBN
978-3-662-46224-9
978-3-662-46223-2
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.
DDC Class
004: Informatik
610: Medizin