Bioimpedance Spectroscopy Measurement and Classification of Lung Tissue to Identify Pulmonary Nodules

• Published in: IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-7
• Main Authors: Baghbani, Rasool, Shadmehr, Mohammad Behgam, Ashoorirad, Masoomeh, Molaeezadeh, Seyyedeh Fatemeh, Moradi, Mohammad Hassan
• Format: Article
• Language: English
• Subjects: Bioimpedance; Classification; Classifiers; Computed tomography; Data classification; Discriminant analysis; electrical bio-impedance spectrum; Electrodes; Frequency ranges; Impedance measurement; Lung; Lung cancer; Medical screening; minimally invasive surgery; Nodules; Nyquist curve; Principal components analysis; Probes; pulmonary nodule; Support vector machines; Surgery
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3105241

Lung cancer is the most common and lethal cancer in many parts of the world. The establishment of lung cancer screening by low-dose computerized tomography (CT) scan has led to finding lung cancers in early stages as very small nodules. However, finding those nodules, particularly when located deep in the lung parenchyma, could be impossible during lung surgeries without preoperative or intraoperative localization. This study introduces a simple and safe method having the potential to localize in-depth pulmonary nodules intraoperatively. In this regard, a bioimpedance probe with four spherical electrodes was designed and built. By an in vitro study, the bioimpedance data of 286 lung tissue samples obtained from 38 patients in a frequency range of 50 kHz-5 MHz were collected and analyzed with Nyquist curves and boxplot charts. Finally, a smart system was designed based on the bioimpedance phase and magnitude to differentiate healthy lung tissue from the tumoral lung tissue. Our proposed system consists of two parts: the feature reduction with principal component analysis (PCA) and the classification with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN). Classifier analysis showed that the accuracy of all classifiers was more than 95% for 15 principal components; the SVM classifiers had the highest accuracy above 98%. This research sheds light on the feasibility of designing a real-time, safe, and smart system to localize the invisible/impalpable pulmonary nodules by the bioimpedance spectrum of the lung tissue.