Effects of Tourniquets in the Development of Pain States: a Novel Clinical Pilot Study and Review of Utilization of Tissue Oximetry to Measure Neural Ischemia

Pyati, Srinivas; Cobert, Julien; Jabre, Joe F.; Kaye, Alan D.; Diaz, James H.; Raghunathan, Karthik

doi:10.1007/s11916-020-00855-x

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…As early as 2007, there was a study using near-infrared spectroscopy (NIRS) to evaluate the difference in regional cerebral blood flow (rCBF) changes of the middle cerebral artery between migraine patients and the healthy control group during the breath-holding task 7 . In recent years, NIRS has gradually emerged in the pain field 8 – 11 . Moreover, NIRS has the advantages of non-invasive, non-radioactive, instant, low system cost, portability and easy operability, etc.…”

Section: Methodsmentioning

confidence: 99%

Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task

Chen

Hsieh

Liu

et al. 2022

Sci Rep

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Migraine is a common and complex neurovascular disorder. Clinically, the diagnosis of migraine mainly relies on scales, but the degree of pain is too subjective to be a reliable indicator. It is even more difficult to diagnose the medication-overuse headache, which can only be evaluated by whether the symptom is improved after the medication adjustment. Therefore, an objective migraine classification system to assist doctors in making a more accurate diagnosis is needed. In this research, 13 healthy subjects (HC), 9 chronic migraine subjects (CM), and 12 medication-overuse headache subjects (MOH) were measured by functional near-infrared spectroscopy (fNIRS) to observe the change of the hemoglobin in the prefrontal cortex (PFC) during the mental arithmetic task (MAT). Our model shows the sensitivity and specificity of CM are 100% and 75%, and that of MOH is 75% and 100%.The results of the classification of the three groups prove that fNIRS combines with machine learning is feasible for the migraine classification.

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Section: Methodsmentioning

confidence: 99%