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Anomaly Detection of Respiratory Motion by Use of Singular Spectrum Analysis

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J Kotoku

J Kotoku1*, S Kumagai1 , S Nakabayashi1 , A Haga2 , T Kobayashi1 , (1) Teikyo University, Tokyo, Japan, (2) University of Tokyo Hospital, Tokyo, Japan


SU-G-JeP4-3 (Sunday, July 31, 2016) 5:30 PM - 6:00 PM Room: ePoster Theater

The implementation and realization of automatic anomaly detection of respiratory motion is a very important technique to prevent accidental damage during radiation therapy.
Here, we propose an automatic anomaly detection method using singular value decomposition analysis.

The anomaly detection procedure consists of four parts:
1) measurement of normal respiratory motion data of a patient
2) calculation of a trajectory matrix representing normal time-series feature
3) real-time monitoring and calculation of a trajectory matrix of real-time data.
4) calculation of an anomaly score from the similarity of the two feature matrices.
Patient motion was observed by a marker-less tracking system using a depth camera.

Two types of motion e.g. cough and sudden stop of breathing were successfully detected in our real-time application.

Automatic anomaly detection of respiratory motion using singular spectrum analysis was successful in the cough and sudden stop of breathing. The clinical use of this algorithm will be very hopeful.

Funding Support, Disclosures, and Conflict of Interest: This work was supported by JSPS KAKENHI Grant Number 15K08703.

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