<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The purpose of this paper is to identify differences between abnormal and normal lung signals gathered by an EIT device, which is a new, non-invasive system that seeks the electrical conductivity and permittivity inside a body. Lung performances in patients are investigated using Phase Space Mapping technique on Electrical EIT signals. The database used in this paper contains 82 registered records of 52 individuals with proper lung volume. The results of this paper show that as the delay parameter (τ) increases, the SD1 parameter of phase space mapping indicates a significant difference between normal and abnormal lung volumes. The value of the SD1 parameter with τ = 6 in the case that the lung volume is in a normal condition is 342.57 ± 32.75 while it is 156.71 ± 26.01 in non-optimal mode. This method can be used to identify the patients’ lung volumes with chronic respiratory illnesses and is an accurate assessment of the diverse methods to treat respiratory system illnesses in addition to saving various therapeutic costs and dangerous consequences that are likely to occur by using improper treatment methods. It can also reduce the required treatment durations.</p></abstract>

The purpose of this paper is to identify differences between abnormal and normal lung signals gathered by an EIT device, which is a new, non-invasive system that seeks the electrical conductivity and permittivity inside a body. Lung performances in patients are investigated using Phase Space Mapping technique on Electrical EIT signals. The database used in this paper contains 82 registered records of 52 individuals with proper lung volume. The results of this paper show that as the delay parameter (τ) increases, the SD1 parameter of phase space mapping indicates a significant difference between normal and abnormal lung volumes. The value of the SD1 parameter with τ = 6 in the case that the lung volume is in a normal condition is 342.57 ± 32.75 while it is 156.71 ± 26.01 in non-optimal mode. This method can be used to identify the patients’ lung volumes with chronic respiratory illnesses and is an accurate assessment of the diverse methods to treat respiratory system illnesses in addition to saving various therapeutic costs and dangerous consequences that are likely to occur by using improper treatment methods. It can also reduce the required treatment durations.

Recognition of lung volume condition based on phase space mapping using electrical impedance tomography

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The purpose of this paper is to identify differences between abnormal and normal lung signals gathered by an EIT device, which is a new, non-invasive system...

Lung Volume Conditions	SD1	SD2
Lung Volume Conditions	Mean ± Standard deviation	Mean ± Standard deviation
Unacceptable	156.71 ± 26.01	2457.98 ± 245.54
Suitable	342.32 ± 57.75	2113.18 ± 230.67

Lung Volume Conditions	SD1	SD2
Lung Volume Conditions	Mean ± Standard deviation	Mean ± Standard deviation
Unacceptable	118.19 ± 15.75	2217.25 ± 228.51
Suitable	158.09 ± 34.12	1835.12± 216.41

Lung Volume Conditions	SD1	SD2
Lung Volume Conditions	Mean ± Standard deviation	Mean ± Standard deviation
Unacceptable	96.51 ± 12.10	2104.23 ± 223.25
Suitable	112.67 ± 24.09	1716.06± 213.43

Lung Volume Conditions	SD1	SD2
Lung Volume Conditions	Mean ± Standard deviation	Mean ± Standard deviation
Unacceptable	148.20 ± 24.17	2418.23 ± 239.67
Suitable	297.41 ± 49.25	2098.27 ± 226.15

Lung Volume Conditions	SD1	SD2
Lung Volume Conditions	Mean ± Standard deviation	Mean ± Standard deviation
Unacceptable	132.54 ± 21/14	2375.78 ± 234.01
Suitable	216.37 ± 41/62	1932.64± 220.17

Recognition of lung volume condition based on phase space mapping using electrical impedance tomography

Published Online: Aug 20, 2019

Page range: 34 - 39

Received: Dec 02, 2018

DOI: https://doi.org/10.2478/joeb-2019-0005

Keywords
lung volume, electrical impedance tomography, signal processing, phase space mapping

© 2019 Moridani, Choopani, Kia. published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig 6

Fig. 7

The average and the standard deviation of the SD1 and SD2 parameters with τ = 6.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 3.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 2.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 5.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 4.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 1.

Recognition of lung volume condition based on phase space mapping using electrical impedance tomography

Published Online: Aug 20, 2019

Page range: 34 - 39

Received: Dec 02, 2018

DOI: https://doi.org/10.2478/joeb-2019-0005

Keywordslung volume, electrical impedance tomography, signal processing, phase space mapping

© 2019 Moridani, Choopani, Kia. published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig 6

Fig. 7

The average and the standard deviation of the SD1 and SD2 parameters with τ = 6.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 3.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 2.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 5.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 4.

The average and the standard deviation of the SD1 and SD2 parameters with τ = 1.

Keywords
lung volume, electrical impedance tomography, signal processing, phase space mapping