COVID-19 detection using a model of photoplethysmography (PPG) signals. (November 2022)

• Record Type: Journal Article
• Authors: Rossi, Eva; Aliani, Cosimo; Francia, Piergiorgio; Deodati, Rossella; Calamai, Italo; Luchini, Marco; Spina, Rosario; Bocchi, Leonardo
• Is Part Of: Medical engineering & physics. Volume 109(2022)
• Publication Date: 2022-11
• Subjects: Microcirculation -- COVID-19 -- Modelling -- PPG; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2022.103904
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: PPG signals modelling was validated for detecting the infection from Covid-19. Fitting procedures reaches about 98% both on Control signals and Patient signals. Bayes classifier reaches an accuracy of 79% in the distinction between Control and moderate Covid-19 groups. High values of sensibility make this method promising for a quick screening procedure. Abstract: Objective: Coronavirus disease 2019 (COVID-19) targets several tissues of the human body; among these, a serious impact has been observed in the microvascular system. The aim of this study was to verify the presence of photoplethysmographic (PPG) signal modifications in patients affected by COVID-19 at different levels of severity. Approach: The photoplethysmographic signal was evaluated in 93 patients with COVID-19 of different severity (46: grade 1; 47: grade 2) and in 50 healthy control subjects. A pre-processing step removes the long-term trend and segments of each pulsation in the input signal. Each pulse is approximated with a model generated from a multi-exponential curve, and a Least Squares fitting algorithm determines the optimal model parameters. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested to discriminate among healthy controls and patients with COVID, stratified according to the severity of the disease. Results are validated with the leave-one-subject-out validation method. Main results: Results indicate that the fitting procedure obtains a very high determination coefficient (above 99% in both controls and pathological subjects). The proposed Bayesian classifier obtains promising results, given the size of the dataset, and variable depending on the classification strategy. The optimal classification strategy corresponds to 79% of accuracy, with 90% of specificity and 67% of sensibility. Significance: The proposed approach opens the possibility of introducing a low cost and non-invasive screening procedure for the fast detection of COVID-19 disease, as well as a promising monitoring tool for hospitalized patients, with the purpose of stratifying the severity of the disease.