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Revistas
Journal of Electrical Bioimpedance
Volumen 11 (2020): Edición 1 (January 2020)
Acceso abierto
Design of a drop-in EBI sensor probe for abnormal tissue detection in minimally invasive surgery
Guanming Zhu
Guanming Zhu
,
Liang Zhou
Liang Zhou
,
Shilong Wang
Shilong Wang
,
Pengjie Lin
Pengjie Lin
,
Jing Guo
Jing Guo
,
Shuting Cai
Shuting Cai
,
Xiaoming Xiong
Xiaoming Xiong
,
Xiaobing Jiang
Xiaobing Jiang
y
Zhuoqi Cheng
Zhuoqi Cheng
| 31 dic 2020
Journal of Electrical Bioimpedance
Volumen 11 (2020): Edición 1 (January 2020)
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Publicado en línea:
31 dic 2020
Páginas:
87 - 95
Recibido:
22 sept 2020
DOI:
https://doi.org/10.2478/joeb-2020-0013
Palabras clave
Laparoscopic surgery
,
drop-in sensor probe
,
electrical bioimpedance spectroscopy
,
discriminant analysis
© 2020 Guanming Zhu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Fig.1
The proposed sensor probe aims to assist in the MIS for tissue detection. The probe connects to an EBI meter for measurement and the sensing data is streaming to a laptop for processing and display.
Fig.2
(A) The model of the electrical bioimpedance measured by the proposed sensor probe; (B) Modelling the low frequency and high frequency current passing through biological tissue.
Fig.3
(A) The electrode pattern; (B) Modelling the EBI sensing of soft tissue with the designed electrode; (C) the current density simulation of the section indicated in (A).
Fig.4
The prototype of the sensor probe, which can be grasped by a surgical forceps for measuring the area of interest.
Fig.5
The experimental setup including an LCR meter, tissue samples and the designed sensor probe which is grasped by a surgical forceps.
Fig.6
Experimental results of 5 saline solutions in different excitation frequencies with the sensor probe.
Fig.7
Experimental results of ex vivo porcine liver tissues including the capacitance and resistance: the blue line indicates the results of normal tissue and the red line indicates the results of the abnormal tissue.
Confusion Matrix showing the classification results using the QDA classifier.
Abnormal
Normal
Abnormal
12
8
Normal
0
20