Lung cancer detection via breath by electronic nose enhanced with a sparse group feature selection approach

• Published in: Sensors and actuators. B, Chemical Vol. 339; p. 129896
• Main Authors: Liu, Bei, Yu, Huiqing, Zeng, Xiaoping, Zhang, Dan, Gong, Juan, Tian, Ling, Qian, Junhui, Zhao, Leilei, Zhang, Shuya, Liu, Ran
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.07.2021; Elsevier Science Ltd
• Subjects: Age factors; Breath analysis; Classification; Data collection; Electronic nose (E-nose); Electronic noses; Feature extraction; Lung cancer; Performance enhancement; Sensor arrays; Sensors; Sparse group lasso (SGL); Statistical analysis; Subsystems; Thermal desorption; VOCs; Volatile organic compounds; Volatile organic compounds (VOCs); Thermal desorption; Electronic nose (E-nose); Sparse group lasso (SGL); Lung cancer; Breath analysis; Volatile organic compounds (VOCs)
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2021.129896

•An E-nose platform with a novel thermal desorption pre-concentration subsystem is built for LC patients identification.•Patients with LC/benign pulmonary diseases and healthy ones are distinguished via breath analysis.•A sparse group feature selection method improves the effect of classification (up to 12 %).•No significant influences are found for the effect of age and smoking habit on classification results. In the diagnosis of lung cancer, electronic nose (E-nose) has attracted much attention by detecting the volatile organic compounds (VOCs) in exhaled breath. In this research, an E-nose platform with a novel thermal desorption preconcentration subsystem is designed to verify whether analyzing VOCs can reliably differentiate lung cancer patients from healthy individuals and patients with benign pulmonary diseases. To this end, total 87 subjects (46 patients with lung cancer, 36 healthy volunteers and 5 patients with benign pulmonary diseases) are enrolled for the sensor array data collection. 13 composite features are extracted from each sensor, and some classical classifiers are established to demonstrate the feasibility of identifying lung cancer patients through VOCs. To improve the performance, considering the inherent characteristics of E-nose data, a sparse group feature selection (FS) method is applied to the raw sensor array data. It is observed that the FS method can reduce data dimensionality and improve the classification performance significantly. Statistical analysis for the effect of age and smoking habit on classification results shows that no significant influences are found.