Exploring New Horizons: fNIRS and Machine Learning in Understanding PostCOVID-19

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Dades bibliogràfiques
Publicat a:	Machine Learning and Knowledge Extraction vol. 7, no. 4 (2025), p. 129-150
Autor principal:	Morales-Cervantes, Antony
Altres autors:	Herrera, Victor, Zamora-Mendoza, Blanca Nohemí, Flores-Ramírez, Rogelio, López-Cano, Aaron A, Guevara, Edgar
Publicat:	MDPI AG
Matèries:	Infections Feature extraction Neuroimaging Alzheimer's disease Long COVID Datasets Hemodynamics Multilayers Severe acute respiratory syndrome coronavirus 2 Asymptomatic Brain research Multilayer perceptrons Medical imaging Cognitive ability Machine learning Statistical analysis Blood Infrared spectra Representations Hemoglobin Spectrum analysis Sensitivity Support vector machines Near infrared radiation Spectroscopy Biomarkers Portability Real time Executive function Time series
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Resum:	PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiers—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)—were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifier trained on hybrid features achieved strong discrimination (<inline-formula>ROC-AUC</inline-formula> = 0.909) under subject-aware CV5; at the default threshold, <inline-formula>Sensitivity</inline-formula> was moderate and <inline-formula>Specificity</inline-formula> was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited <inline-formula>Sensitivity</inline-formula> despite high <inline-formula>Specificity</inline-formula>. These findings highlight the importance of temporal information in the fNIRS signal and support the potential of machine learning combined with portable neuroimaging for postCOVID-19 identification. This approach may contribute to the development of non-invasive diagnostic tools to support individualized treatment and longitudinal monitoring of patients with persistent neurological symptoms.
ISSN:	2504-4990
DOI:	10.3390/make7040129
Font:	Advanced Technologies & Aerospace Database