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The realization of automatic anomaly detection of respiratory motion could be very useful to prevent accidental damage during radiation therapy. In this paper, we proposed an automatic anomaly detection method using singular value decomposition analysis. Before applying this method, the investigator needs a normal respiratory motion data of a patient. From these data, a trajectory matrix representing normal time-series feature is created. Decomposing the matrix, we obtained the feature of normal time series. Then, we applied the same procedure to real-time data and obtained real-time features. Calculating the similarity of those feature matrixes, an anomaly score was obtained. Patient motion was observed by a depth camera. In our simulation, two types of motion e.g. cough and sudden stop of breathing were successfully detected, while gradual change of respiratory cycle frequency was not detected clearly.

Monitoring of a patient has been an important but tedious work for medical staffs. The automatic anomaly detection for respiratory motion is very critical to conduct radiation therapy safely.

The difficulty of anomaly detection causes from the fact that 1) almost all of respiratory curves show base- line drift, 2) amplitudes of respiratory motions vary sometimes up to 300%, 3) frequency of respiratory cycle sometimes changes [

There has been some useful anomaly detection algorithm for industrial use. Those algorithms are classified into two categories: model based algorithm and model free algorithm. Typical examples of model based approach are auto regressive (AR) model [

In this paper, we tried to apply an anomaly detection algorithm based on singular spectrum analysis to a respiratory curve of a patient.

In this study, we applied the singular decomposition technique to measured respiratory motion obtained by a depth camera (Microsoft Kinect v1). First, we will describe how we measured respiratory curve. In the next section, we will explain the detail of algorithm to detect anomaly motion.

To monitor the respiratory motion, contact-less sensor was desirable. As a low-price sensor, our group has been using a Kinect sensor. Kinect sensor obtained depth data to a target from a shift of projected infrared patterns and the accuracy was 3 - 4 mm. To reduce the noise, we applied Ueda’s algorithm to the raw data of Kinect sensor. The sampling rate was 5 fps. The detail of this system was described in Kumagai et al. [

For testing performance, one of the authors simulated two types of anomaly motion, e.g. cough, sudden stop of breathing.

To detect an anomaly motion, we first extracted the feature of the time-series data with window length w based on singular value spectrum analysis [

Let normal (or reference) time-series data be

The matrix holds the feature of normal time series data at a reference time

Similarly, we can define a matrix representing current states (at time t) of respiratory motion as,

For example, window length w was set to 40 (= 8 sec). k was set to

Next, we decomposed the matrix

here

From this decomposition of matrix, the matrix

This is an example of well-known spectrum decomposition of a matrix. For

Then we defined a matrix representing the feature of time series of the reference signals as

Similarly, a matrix representing features of current status was defined as

To measure the similarity of two time series, we calculated canonical angle of two matrices as

where

This score represents the anomaly of the current time window compared with the reference time window.

The anomaly detection can be divided into six steps.

1. Measurement of normal (not including anomaly events) respiratory motion.

2. Calculation of

3. Measurement of respiratory motion.

4. Calculation of

5. Calculation of anomaly score from

6. If the anomaly score exceeds the threshold, then the algorithm judged anomaly event occurred.

In the Step1, we measured a respiratory curve of a patient under normal situation (not suffered from anomaly events).

To test our algorithm, we measured the reference respiratory motion and normal respiratory motion by a volunteer. Then the time-series data were retrospectively analyzed using our algorithm. All algorithms were implemented using R language [

Based on the algorithm, we performed real-time prediction of respiratory motion using our respiratory monitoringsystem [

In our study, we applied an anomaly detection algorithm based on singular spectrum analysis.

Measurement data was obtained by a depth camera.

As a reference of normal respiratory curve, a normal respiratory motion was recorded with a depth camera. The obtained data was shown in

To simulate anomaly motions, the four types of anomaly motion were tested.

can be seen, the two types of events (cough and sudden stop of breathing) was successfully detected as a sudden increase of anomaly score. On the other hand, gradual increase of respiratory period was not clearly detected by this method.

Real-time anomaly detection was tested as shown in

Prior works have suggested that a general algorithm to detect anomaly motion would be difficult. This paper tried to overcome the difficulty using singular spectrum analysis.

The window length w is a main parameter of the analysis. The result depends on the parameter. However, there has been no universal selection rule. Therefore, we analyzed a respiratory curve with different length and compared them by visual inspection.

There has a limitation, however, in this algorithm. One of the demerit in our method is the difficulty of the simple and explicit interpretation of the anomaly model. There has no clear-cut threshold to alarm an error. Users should be aware of these difficulties.

The merit of this approach is the fact that no a priori modeling of anomaly motion is required. Because anomaly motion is defined as a deviation from the normal motion, an explicit pre-implementation of anomaly mode is considered to be difficult. Using this algorithm, an unexpected anomaly motion could be detected. Hence, this algorithm will be a good supporting tool for medical staffs.

We have demonstrated that 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.

This work was supported by JSPS KAKENHI Grant Number 15K08703 and partly supported by JSPS Core-to- Core Program (No. 23003).

Jun’ichi Kotoku,Shinobu Kumagai,Ryouhei Uemura,Susumu Nakabayashi,Takenori Kobayashi, (2016) Automatic Anomaly Detection of Respiratory Motion Based on Singular Spectrum Analysis. International Journal of Medical Physics, Clinical Engineering and Radiation Oncology,05,88-95. doi: 10.4236/ijmpcero.2016.51009