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Zeitschriften
Journal of Electrical Bioimpedance
Band 12 (2021): Heft 1 (January 2021)
Uneingeschränkter Zugang
Electrical impedance characterization of
in vivo
porcine tissue using machine learning
Stephen Chiang
Stephen Chiang
,
Matthew Eschbach
Matthew Eschbach
,
Robert Knapp
Robert Knapp
,
Brian Holden
Brian Holden
,
Andrew Miesse
Andrew Miesse
,
Steven Schwaitzberg
Steven Schwaitzberg
und
Albert Titus
Albert Titus
| 02. Juli 2021
Journal of Electrical Bioimpedance
Band 12 (2021): Heft 1 (January 2021)
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Online veröffentlicht:
02. Juli 2021
Seitenbereich:
26 - 33
Eingereicht:
21. März 2021
DOI:
https://doi.org/10.2478/joeb-2021-0005
Schlüsselwörter
Bioimpedance
,
surgical staplers
,
tissue characterization
© 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
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.
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.
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.
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.
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.