Signal Analysis and Detection of COVID-19 Infection with ATR-FTIR Spectroscopy

COVID-19 exhibits diverse transmission routes and an extended incubation period, facilitating rapid dispersion across broad geographical regions. Therefore, the process of conducting COVID-19 detection holds utmost significance. Inspired by the idea of spectral signal-to-map, we introduce the Competitive Adaptive Reweighted Sampling-Principal Component Analysis (CARS-PCA) feature extractor in combination with a Convolutional Neural Network-Support Vector Machine (CNN-SVM) model, which aims to classify the infrared spectra obtained from COVID-19 pharyngeal swab samples. By conducting an experimental analysis on a series of infrared spectral signals obtained from positive and negative pharyngeal swabs, the experimental findings, on the one hand, indicate that the CARS-PCA algorithm is better suited for feature selection and reducing the dimensionality of the spectral signal data. On the other hand, they also illustrate the superior performance of the proposed CNN-SVM model in comparison to conventionally employed spectral signal recognition algorithms, achieving an accuracy of 85.38%, a sensitivity of 86.22%, and a specificity of 83.92%.