2023
DOI: 10.1364/boe.489441
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Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods

Abstract: Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients wi… Show more

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Cited by 3 publications
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“…The combination of machine learning techniques and fNIRS signals represents a noteworthy advancement in neurorehabilitation, providing a more nuanced understanding of a patient’s neural status and the customization of interventions. The use of fNIRS in brain monitoring has attracted attention in recent research [ 50 52 ], with studies achieving > 85% accuracy in differentiating between stroke types and healthy individuals using fNIRS data combined with machine learning algorithms [ 53 ]. Moreover, the use of random forest algorithms to analyze fNIRS signals during motor tasks has shown promise in distinguishing patients with major depressive disorder, demonstrating an accuracy of 91.13% [ 54 ].…”
Section: Discussionmentioning
confidence: 99%
“…The combination of machine learning techniques and fNIRS signals represents a noteworthy advancement in neurorehabilitation, providing a more nuanced understanding of a patient’s neural status and the customization of interventions. The use of fNIRS in brain monitoring has attracted attention in recent research [ 50 52 ], with studies achieving > 85% accuracy in differentiating between stroke types and healthy individuals using fNIRS data combined with machine learning algorithms [ 53 ]. Moreover, the use of random forest algorithms to analyze fNIRS signals during motor tasks has shown promise in distinguishing patients with major depressive disorder, demonstrating an accuracy of 91.13% [ 54 ].…”
Section: Discussionmentioning
confidence: 99%