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 proceedingConference article in proceedingsScientificpeer-review

    38 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 languageEnglish
    Title of host publication2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages450-453
    Number of pages4
    ISBN (Electronic)978-1-7281-4617-1
    ISBN (Print)978-1-7281-4618-8
    DOIs
    Publication statusPublished - Oct 2019
    MoE publication typeA4 Article in a conference publication
    Event19th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2019 - Athens, Greece
    Duration: 28 Oct 201930 Oct 2019

    Conference

    Conference19th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2019
    Period28/10/1930/10/19

    Keywords

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

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