**4.3. Result for singularity distinction**

In order to classify the singularity of the surgical operation of (4) insertion, a SOM was used. The SOM was constructed by batch learning using the feature vectors of any singularity of operation pre-extracted from SEMG in the case of insertion. Fig.7 shows the constructed SOM and distribution of the mapping of the feature vectors extracted on-line from SEMG for each experimental operation of insertion. The domain of the SOM is roughly divided into two fields, which include the domain for the normal operation denoted as "Normal" and the domain for the singular operation denoted as "Singular." In addition, the domain for the singular operation is divided into three fields, namely, "Posture," "Straining," and "Sudden."

**Figure 7.** Distribution of experimental operation on SOM

The number of the hexagon counted in each field on the map is shown in Table 5.


Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals 257

**Table 5.** The number of hexagon counted in each field on SOM

Computational Intelligence in Electromyography Analysis – 256 A Perspective on Current Applications and Future Challenges

**Table 4.** Threshold values

**4.3. Result for singularity distinction** 

**Singular**

**Figure 7.** Distribution of experimental operation on SOM

accuracy.

The threshold value *THi* (*i*=1,2,3*H*, 3*L*) determined through trial and error is shown in Table 4.

Threshold Value *TH*<sup>1</sup> 0.045 *TH*<sup>2</sup> 0.5×10-9 *TH*3<sup>H</sup> 0.25 *TH*3<sup>L</sup> 0.2

As shown in Table 3, each surgical operation could be identified with more than 80%

In order to classify the singularity of the surgical operation of (4) insertion, a SOM was used. The SOM was constructed by batch learning using the feature vectors of any singularity of operation pre-extracted from SEMG in the case of insertion. Fig.7 shows the constructed SOM and distribution of the mapping of the feature vectors extracted on-line from SEMG for each experimental operation of insertion. The domain of the SOM is roughly divided into two fields, which include the domain for the normal operation denoted as "Normal" and the domain for the singular operation denoted as "Singular." In addition, the domain for the singular operation is divided into three fields, namely, "Posture," "Straining," and "Sudden."

**Straining**

**Sudden Posture**

The number of the hexagon counted in each field on the map is shown in Table 5.

**Normal**

**Normal**

Recognition rate for singularity distinction is shown in Table 6.


**Table 6.** Recognition rate for singularity distinction

As shown in Table 5, the normal and the singular operation of insertion could be distinguished with 76.5% and 81.8% accuracy, respectively. However, the accuracy of recognition of the singularity (i.e., "Posture," "Straining," or "Sudden") of the operation is approximately 30%.

As one of the reasons of this low recognition rate in the singularity distinction, the following cause is considered. In the states of "Posture", "Straining" and "Sudden", the singular operation is similar, and the difference does not appear easily in the feature vector.

In order to examine efficiency of each feature, namely average absolute value, center-ofgravity and spectrum ratio, in the feature vector defined by equation (13), singular operation was recognized by SOM using the three-dimensional feature vector which consists of each feature only. Singularity recognition rate for each feature is shown in Table 7.

From Table 7, it turns out that the average absolute value contributes to distinction of normal operation compared with the center-of-gravity and the spectrum ratio, and conversely, the center-of-gravity and the spectrum ratio contribute to the whole singularity distinction compared with the average absolute value.

Based on the above result, to raise the singularity recognition rate in each state (Posture, Straining, and Sudden), singularity distinction was performed repeatedly by combining three kinds of features in the feature vector (Average absolute value, Center-of-gravity, and Spectrum ratio) through trial and error.

As a result, the best singularity recognition rate was obtained for the following sixdimensional feature vector removing the spectrum ratio.

$$\text{Xs} = \left( \frac{\text{MAV}\_1}{\text{MAV}}, \frac{\text{MAV}\_2}{\text{MAV}}, \frac{\text{MAV}\_3}{\text{MAV}}, \frac{\text{cog}\_2}{\text{cog}\_1}, \frac{\text{cog}\_3}{\text{cog}\_1}, \frac{\text{cog}\_3}{\text{cog}\_2} \right)^T \tag{14}$$

Then, two operators were added and the singularity distinction was performed by SOM using the feature vector defined by (14). Recognition rate for singularity distinction using the feature vector given by (14) is shown in Table 8.

Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals 259

On the other hand, recognition rate of each state in the singular operation was approximately 30% to 90% accuracy depending on the individual difference. Therefore, it is difficult to

However, in a complicated surgical operation such as insertion of a needle, it can be said that general distinction of normal operation or singular operation was able to be recognized

In this study, operator for the experiments was only three persons. In order to demonstrate the reliability of the proposed automatic identification and singularity distinction method, it is necessary to perform verification of the proposed method by many operators. However, since SEMG depends on the individuals, it is considered that learning of the SOM for

In addition, it is also necessary to extend the proposed identification and singularity distinction method for a surgical operation performed with not only a right hand but also both hands. As for this point, we are now applying the proposed identification method to a surgical operation of ligation performed with both hands, and the singularity distinction

Furthermore, construction of the system to avoid malpractice by presenting recognition of the singular operation to the operator and to provide safe endoscopic-surgery is left as future work.

The part of this work was supported by Grant-in-Aid for Scientific Research (23650100). The

Chen, X.; Zhang, X.; Zhao, Z.Y. & Yang, J.H. (2007). Multiple Hand Gesture Recognition based on Surface EMG Signal, *Proceedings of International Conference on Bioinformatics and* 

Harada, A.; Ishii, C.; Nakakuki, T. & Hikita, M. (2010). Robot Finger Design for Myoelectric Prosthetic Hand and Recognition of Finger Motions via Surface EMG, *Proceedings of* 

Hashizume, M.; Konishi, K.; Okazaki, K. & Tanoue, K. (2005). Fundamental Training for safe

Hayama, Y.; Kurita, Y.; Kawahara, T.; Okajima, M. & Ogasawara, T. (2009). Automatic Measurement of Forceps Manipulation Logs for Laparoscopic Surgery, Journal of Japan

*2010 IEEE International Conference on Automation and Logistics*, pp. 273-278

Society of Computer Aided Surgery, Vol.11, No.3, pp. 328-329 (in Japanese)

distinguish three kinds of the states in the singular operation with sufficient accuracy.

with high accuracy.

**Author details** 

*Hosei University, Japan* 

**Acknowledgement** 

*Biomedical Engineering*, pp. 506-509

Chiharu Ishii

**7. References** 

**6. Future directions** 

singularity distinction for every operator is required.

method to a thread knotting also performed with both hands.

author thanks Y. Nakaya for his assistance in experimental works.

Endoscopic Surgery, Daidogakkan Press (in Japanese)


**Table 7.** Singularity recognition rate for each feature


**Table 8.** Modified recognition rate for singularity distinction

From Table 8, for the operators B and C, the singularity recognition rate for "Posture" and "Straining" was improved.
