Detecting artifacts in signals is an important problem in a wide number of research areas. In robotic radiotherapy motion predic- tion 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 in- troduce 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.
['Sven-Thomas Antoni', 'Robert Plagge', 'Robert Dürichen', 'Alexander Schlaefer']
Detecting Respiratory Artifacts from Video Data