Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients.

BACKGROUND: Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. METHODS: This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. RESULTS: Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. CONCLUSIONS: The study's results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects.

Automatic COVID-19 severity assessment from HRV.

COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group's comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups' comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation.

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

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

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.

Vascular ageing and peripheral pulse: an improved model for assessing their relationship.

Objective.Vascular ageing is associated with several alterations, including arterial stiffness and endothelial dysfunction. Such alterations represent an independent factor in the development of cardiovascular disease (CVD). In our previous works we demonstrated the alterations occurring in the vascular system are themselves reflected in the shape of the peripheral waveform; thus, a model that describes the waveform as a sum of Gaussian curves provides a set of parameters that successfully discriminate betweenunder(≤35 years old) andoversubjects (>35 years old). In the present work, we explored the feasibility of a new decomposition model, based on a sum of exponential pulses, applied to the same problem.Approach.The first processing step extracts each pulsation from the input signal and removes the long-term trend using a cubic spline with nodes between consecutive pulsations. After that, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximates each single pulse. The vector of model parameters gives a compact representation of the pulse waveform that constitutes the basis for the classification step. Each subject is associated to his/her 'representative' pulse waveform, obtained by averaging the vector parameters corresponding to all pulses. Finally, a Bayesan classifier has been designed to discriminate the waveforms of under and over subjects, using the leave-one-subject-out validation method.Main results.Results indicate that the fitting procedure reaches a rate of 96% in under subjects and 95% in over subjects and that the Bayesan classifier is able to correctly classify 91% of the subjects with a specificity of 94% and a sensibility of 84%.Significance.This study shows a sensible vascular age estimation accuracy with a multi-exponential model, which may help to predict CVD.