Local warning integrated with global feature based on dynamic spectra for FAIMS data analysis in detection of clinical wound infection

• Published in: Sensors and actuators. B, Chemical Chemical, 2019-11, Vol.298, p.126926, Article 126926
• Main Authors: Sun, Tong, Tian, FengChun, Bi, YuTian, Zhong, XiaoZheng, He, Jiao, Yang, TaiCong, Guo, QingShan, Lei, Ying, Lu, YanYi, Zeng, Lin, He, QingHua
• Format: Article
• Language: English
• Subjects: Algorithms; Data analysis; E coli; Economic conditions; Economic impact; Escherichia coli; Field Asymmetric Ion Mobility Spectrometry (FAIMS); Infections; Ionic mobility; Machine learning; Public health; Wound infection
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2019.126926

•A novel algorithm named LWGF (Local Warning integrated with Global Feature) was proposed for FAIMS data analysis.•The LWGF identified 26 clinical patients (6 uninfected, 20 E. coli-infected) with the best average AUC of 0.98, and recognition rate of 96.15%.•For FAIMS data analysis, we have proven that it is better to use dynamic spectra rather than several stable spectra. Infections have long been a thorny problem that severely threatened public health and resulted in tremendous economic losses worldwide. Current detection methods for wound infection do not fully meet the requirements of preventing and treating this disease. Therefore, people are looking for better alternatives, wherein FAIMS (Field Asymmetric Ion Mobility Spectrometry) technology, by virtue of its high sensitivity, rapid response and noninvasive operation, is a promising candidate. This paper aims to investigate the possibility of FAIMS technology in detecting wound infections quickly and accurately. For this purpose, we gathered an odor dataset of clinical wound samples with the employment of a FAIMS instrument, the Lonestar (Owlstone, UK) analyzer. To enhance detectability, we proposed a novel algorithm framework, i.e., Local Warning integrated with Global Feature (LWGF), which is verified on distinguishment between twenty patients with single or mixed infection of Escherichia coli (E. coli) and six wounded patients without infection. Experimental results showed that the LWGF successfully identified the patients with the best average AUC of 0.98, and the best recognition rate of 96.15%, which are much higher than the conventional methods.