Exploring New Horizons: fNIRS and Machine Learning in Understanding PostCOVID-19
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| Publicado en: | Machine Learning and Knowledge Extraction vol. 7, no. 4 (2025), p. 129-150 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make7040129 |2 doi | |
| 035 | |a 3286316347 | ||
| 045 | 2 | |b d20251001 |b d20251231 | |
| 100 | 1 | |a Morales-Cervantes, Antony |u Department of Postgraduate Studies and Research, TecNM—Instituto Tecnológico de Morelia, Av. Tecnológico 1500, Morelia 58120, Michoacán, Mexico; antony.mc@morelia.tecnm.mx | |
| 245 | 1 | |a Exploring New Horizons: fNIRS and Machine Learning in Understanding PostCOVID-19 | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Infections | ||
| 653 | |a Feature extraction | ||
| 653 | |a Neuroimaging | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a Long COVID | ||
| 653 | |a Datasets | ||
| 653 | |a Hemodynamics | ||
| 653 | |a Multilayers | ||
| 653 | |a Severe acute respiratory syndrome coronavirus 2 | ||
| 653 | |a Asymptomatic | ||
| 653 | |a Brain research | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Medical imaging | ||
| 653 | |a Cognitive ability | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Blood | ||
| 653 | |a Infrared spectra | ||
| 653 | |a Representations | ||
| 653 | |a Hemoglobin | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Sensitivity | ||
| 653 | |a Support vector machines | ||
| 653 | |a Near infrared radiation | ||
| 653 | |a Spectroscopy | ||
| 653 | |a Biomarkers | ||
| 653 | |a Portability | ||
| 653 | |a Real time | ||
| 653 | |a Executive function | ||
| 653 | |a Time series | ||
| 700 | 1 | |a Herrera, Victor |u Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología—CIACYT, Universidad Autónoma de San Luis Potosí, Sierra Leona 550, San Luis Potosí 78120, San Luis Potosí, Mexico; super_herrera@hotmail.com (V.H.); lopezcanoazael@gmail.com (A.A.L.-C.) | |
| 700 | 1 | |a Zamora-Mendoza, Blanca Nohemí |u Laboratorio de Salud Total, Centro de Investigación Aplicada en Ambiente y Salud—CIACYT, Universidad Autónoma de San Luis Potosí, Sierra Leona 550, San Luis Potosí 78120, San Luis Potosí, Mexico; blancazamoramendoza@hotmail.com (B.N.Z.-M.); rogelio.flores@uaslp.mx (R.F.-R.) | |
| 700 | 1 | |a Flores-Ramírez, Rogelio |u Laboratorio de Salud Total, Centro de Investigación Aplicada en Ambiente y Salud—CIACYT, Universidad Autónoma de San Luis Potosí, Sierra Leona 550, San Luis Potosí 78120, San Luis Potosí, Mexico; blancazamoramendoza@hotmail.com (B.N.Z.-M.); rogelio.flores@uaslp.mx (R.F.-R.) | |
| 700 | 1 | |a López-Cano, Aaron A |u Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología—CIACYT, Universidad Autónoma de San Luis Potosí, Sierra Leona 550, San Luis Potosí 78120, San Luis Potosí, Mexico; super_herrera@hotmail.com (V.H.); lopezcanoazael@gmail.com (A.A.L.-C.) | |
| 700 | 1 | |a Guevara, Edgar |u Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología—CIACYT, Universidad Autónoma de San Luis Potosí, Sierra Leona 550, San Luis Potosí 78120, San Luis Potosí, Mexico; super_herrera@hotmail.com (V.H.); lopezcanoazael@gmail.com (A.A.L.-C.) | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 7, no. 4 (2025), p. 129-150 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286316347/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286316347/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286316347/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |