Automated Gas Chromatography Peak Alignment: A Deep Learning Approach using Greedy Optimization and Simulation

• Published in: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2023; pp. 1 - 4
• Main Authors: Cao, Loc, Zang, Wenzhe, Sharma, Ruchi, Tabartehfarahani, Ali, Thota, Chandrakalavathi, Devi Sivakumar, Anjali, Lam, Andres, Fan, Xudong, Ward, Kevin R., Ansari, Sardar
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-01-2023
• Subjects: Algorithms; Biological system modeling; Chromatography, Gas - methods; Computer Simulation; COVID-19; Deep Learning; Gas chromatography; Pipelines; Training; Volatile organic compounds; Volatile Organic Compounds - analysis
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10340662

The clinical significance of volatile organic compounds (VOC) in detecting diseases has been established over the past decades. Gas chromatography (GC) devices enable the measurement of these VOCs. Chromatographic peak alignment is one of the important yet challenging steps in analyzing chromatogram signals. Traditional semi-automated alignment algorithms require manual intervention by an operator which is slow, expensive and inconsistent. A pipeline is proposed to train a deep-learning model from artificial chromatograms simulated from a small, annotated dataset, and a postprocessing step based on greedy optimization to align the signals.Clinical Relevance- Breath VOCs have been shown to have a significant diagnostic power for various diseases including asthma, acute respiratory distress syndrome and COVID-19. Automatic analysis of chromatograms can lead to improvements in the diagnosis and management of such diseases.