KEYWORDS: Electronic filtering, Filtering (signal processing), Signal detection, Hemodynamics, Digital filtering, Brain, Signal processing, Image filtering, Sensors, Data acquisition
Cerebral palsy (CP) is the most common motor disorder of central origin in childhood and affects at least 2 children per
1000 live births every year. Neuroimaging techniques are needed to study neuroplastic rearrangements in the human
brain in vivo as a result of CP. Unfortunately, accurate imaging from currently available techniques often requires the
patients' complete body confinement, steadiness and minimal noise for a long period of time, which limits the success
rate to less than 50% for normal children and worse for CP-affected ones. In this work we show that functional near
infrared (fNIR) imaging is robust to motion artifacts and has excellent potential as a sensitive diagnostic tool for this
motor disorder. We have analyzed data from pediatric normal and CP patients performing finger-tapping and handwaving
motor cortex activation tasks. From these analyses we have identified both spatial and temporal metrics of NIR-based
motor cortex activation patterns that can clearly distinguish between normal and CP patients. We also present data
from additional patients where signal processing methods are applied to filter out concurrently recorded hemodynamic
signals due to breathing and cardiac pulsation. It is shown that filtering can substantially improve the quality of
activation data, thus enabling more accurate comparison of activation patterns between normal and CP-affected children.
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