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Electrical impedance characterization of in vivo porcine tissue using machine learning

Stephen Chiang

Stephen Chiang

  • Department of Biomedical Engineering, University at Buffalo, The State University of New YorkBuffalo, USA
  • Medtronic LLCBuffalo, USA
  • Department of Surgery, University at Buffalo, The State University of New YorkBuffalo, USA
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Chiang, Stephen
, 
Matthew Eschbach

Matthew Eschbach

Medtronic LLCBuffalo, USA
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Eschbach, Matthew
, 
Robert Knapp

Robert Knapp

Medtronic LLCBuffalo, USA
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Knapp, Robert
, 
Brian Holden

Brian Holden

Medtronic LLCBuffalo, USA
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Holden, Brian
, 
Andrew Miesse

Andrew Miesse

Medtronic LLCBuffalo, USA
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Miesse, Andrew
, 
Steven Schwaitzberg

Steven Schwaitzberg

Department of Surgery, University at Buffalo, The State University of New YorkBuffalo, USA
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Schwaitzberg, Steven
 y 
Albert Titus

Albert Titus

Department of Biomedical Engineering, University at Buffalo, The State University of New YorkBuffalo, USA
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Titus, Albert
  
02 jul 2021
Electrical impedance characterization of in vivo porcine tissue using machine learning's Cover Image
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Publicado en línea: 02 jul 2021
Páginas: 26 - 33
Recibido: 21 mar 2021
DOI: https://doi.org/10.2478/joeb-2021-0005
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© 2020 Stephen Chiang, Matthew Eschbach, Robert Knapp, Brian Holden, Andrew Miesse, Steven Schwaitzberg, and Albert Titus, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Medtronic's Endo GIA laparoscopic stapler
Medtronic's Endo GIA laparoscopic stapler

Fig. 2

Electrode configuration. Zx represents board impedance measurement. H_CUR and L_CUR are connected to ground electrodes, while H_POT and L_POT are the sense electrodes.
Electrode configuration. Zx represents board impedance measurement. H_CUR and L_CUR are connected to ground electrodes, while H_POT and L_POT are the sense electrodes.

Fig. 3

Validation of measurement system with known resistor values. (a) Modulus values of a control 10 kΩ resistor. (b) Phase angle values of a control 10 kΩ resistor. Phase angle measurements exhibit linear dependency on frequency with slope -2E-5.
Validation of measurement system with known resistor values. (a) Modulus values of a control 10 kΩ resistor. (b) Phase angle values of a control 10 kΩ resistor. Phase angle measurements exhibit linear dependency on frequency with slope -2E-5.

Fig. 4

Electrode placement. (a) is the electrode array that is described in section IIA, and shown in Fig. 2. Four total electrodes are used. The top and bottom white electrodes are H_CUR and L_CUR and are connected to ground electrodes, while the center yellow electrodes are H_POT and L_POT, the sense electrodes. (b) The electrode array is manually applied to the tissue for measurements.
Electrode placement. (a) is the electrode array that is described in section IIA, and shown in Fig. 2. Four total electrodes are used. The top and bottom white electrodes are H_CUR and L_CUR and are connected to ground electrodes, while the center yellow electrodes are H_POT and L_POT, the sense electrodes. (b) The electrode array is manually applied to the tissue for measurements.

Fig. 5

Nyquist plots for EIS measurements. (a) Comparison between all measured tissue types: colon, liver, small bowel, lung, and aggregate stomach. (b) Comparison between just stomach segments (fundus, body, antrum) and all stomach data taken as an aggregate.
Nyquist plots for EIS measurements. (a) Comparison between all measured tissue types: colon, liver, small bowel, lung, and aggregate stomach. (b) Comparison between just stomach segments (fundus, body, antrum) and all stomach data taken as an aggregate.

Fig. 6

(a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
(a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
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1 veces al año
Temas de la revista:
Ingeniería, Bioingeniería e ingeniería biomédica, Electrónica biomédica, Ciencias de la vida, Biofísica, Medicina, Ingeniería biomédica, Física, Espectroscopia y metrología
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