Artifact reduction in long-term monitoring of cerebral hemodynamics using near-infrared spectroscopy

Abstract

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging technique used to assess cerebral hemodynamics. Its portability, ease of use, and relatively low operational cost lend itself well to the long-term monitoring of hemodynamic changes, such as those in epilepsy, where events are unpredictable. Long-term monitoring is associated with challenges including alterations in behaviors and motion that can result in artifacts. Five patients with epilepsy were assessed for interictal hemodynamic changes and alterations in behavior or motion. Based on this work, visual inspection was used to identify NIRS artifacts during a period of interest, specifically prior to seizures, in four patients. A motion artifact reduction algorithm (MARA, also known as the spline interpolation method) was tested on these data. Alterations in the NIRS measurements often occurred simultaneously with changes in motion and behavior. Occasionally, sharp shift artifacts were observed in the data. When artifacts appeared as sustained baseline shifts in the data, MARA reduced the standard deviation of the data and the appearance improved. We discussed motion and artifacts as challenges associated with longterm monitoring of cerebral hemodynamics in patients with epilepsy and our group’s approach to circumvent these challenges and improve the quality of the data collected.