Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data

Emmi Turppa; Inese Polaka; Edgars Vasiljevs; Juha M. Kortelainen; Gidi Shani; Marcis Leja; Hossam Haick

doi:10.1109/BIBE.2019.00087

Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data

Emmi Turppa
, Inese Polaka
, Edgars Vasiljevs
, Juha M. Kortelainen
, Gidi Shani
, Marcis Leja
, Hossam Haick

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

6 Citations (Scopus)

163 Downloads (Pure)

Abstract

The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.

Original language	English
Title of host publication	2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	450-453
ISBN (Electronic)	978-1-7281-4617-1
ISBN (Print)	978-1-7281-4618-8
DOIs	https://doi.org/10.1109/BIBE.2019.00087
Publication status	Published - Oct 2019
MoE publication type	A4 Article in a conference publication
Event	19th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2019 - Athens, Greece Duration: 28 Oct 2019 → 30 Oct 2019

Conference

Conference	19th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2019
Period	28/10/19 → 30/10/19

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme (Euratom research and training programme 2014-2018) under grant agreement No 644031.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breath sensor
Cancer detection
Decision support for health
Gastric cancer
Volatile organic compunds

Access to Document

10.1109/BIBE.2019.00087

sniffphone_bibe19_online_publicAccepted author manuscript, 938 KB

Cite this

Turppa, E., Polaka, I., Vasiljevs, E., Kortelainen, J. M., Shani, G., Leja, M., & Haick, H. (2019). Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 450-453). Article 8941778 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/BIBE.2019.00087

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title = "Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data",

abstract = "The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 \% Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.",

keywords = "Breath sensor, Cancer detection, Decision support for health, Gastric cancer, Volatile organic compunds",

author = "Emmi Turppa and Inese Polaka and Edgars Vasiljevs and Kortelainen, \{Juha M.\} and Gidi Shani and Marcis Leja and Hossam Haick",

note = "Funding Information: This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme (Euratom research and training programme 2014-2018) under grant agreement No 644031. ; 19th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2019 ; Conference date: 28-10-2019 Through 30-10-2019",

year = "2019",

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doi = "10.1109/BIBE.2019.00087",

language = "English",

isbn = "978-1-7281-4618-8",

pages = "450--453",

booktitle = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

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Turppa, E, Polaka, I, Vasiljevs, E, Kortelainen, JM, Shani, G, Leja, M & Haick, H 2019, Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data. in 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)., 8941778, IEEE Institute of Electrical and Electronic Engineers, pp. 450-453, 19th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2019, 28/10/19. https://doi.org/10.1109/BIBE.2019.00087

Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data. / Turppa, Emmi; Polaka, Inese; Vasiljevs, Edgars et al.
2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE Institute of Electrical and Electronic Engineers, 2019. p. 450-453 8941778.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data

AU - Turppa, Emmi

AU - Polaka, Inese

AU - Vasiljevs, Edgars

AU - Kortelainen, Juha M.

AU - Shani, Gidi

AU - Leja, Marcis

AU - Haick, Hossam

N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme (Euratom research and training programme 2014-2018) under grant agreement No 644031.

PY - 2019/10

Y1 - 2019/10

N2 - The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.

AB - The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.

KW - Breath sensor

KW - Cancer detection

KW - Decision support for health

KW - Gastric cancer

KW - Volatile organic compunds

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SN - 978-1-7281-4618-8

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EP - 453

BT - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

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ER -

Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data

Abstract

Conference

Funding

UN SDGs

Keywords

Access to Document

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