Abstract
Introduction: General movements (GM) analysis is used for early detection of movement disorders in high-risk preterms or newborns with complication during delivery, e.g. GM analysis predicts infantile cerebral palsy at an age of 3 months with high sensitivity and specifity. Unfortunately, the implementation in daily clinical practise has not been achieved yet. GM analysis is a video-based analysis of spontaneous movements which needs ample training. Therefore, we investigated a computer-aided approach by using an electromagnetic tracking system (ETS). In previous works we have already demonstrated that ETS is a reliable and practicable way to record GM patterns. The aim of this study was to find methods to differentiate quality of movements. Methods: 30 infants were investigated at an age of three months (in case of preterm infants their age was corrected for their gestational age). Twenty infants were high-risk infants with intracerebral haemorrhage, periventricular leucomalacia or asphyxia. Ten healthy term infants served as controls. The classic video-based GM analysis was used to classify the spontaneous movements as normal or definitely abnormal. Simultaneously, spontaneous movements were recorded with small sensors with 6 degrees of freedom each that were attached to extremities, shoulder and pelvis. Segmental kinematics were calculated by mapping the ETS data to a 3D model. Results: In order to evaluate the variability movement frequency in single joints was plotted against time. This time-frequency-analysis on the one hand illustrated the quantity of movements and on the other hand demonstrated amplitude changes of motion. The amplitude is one aspect of variability and also of complexity of spontaneous movements. Seven infants in the high-risk group were classified as definitely abnormal by classic video-based GM analysis. Five of them showed abnormalities in the time-frequency-analysis. Infants with definitely abnormal GM findings showed movements with high frequencies and high amplitudes, whereas, those with normal GM findings showed movements with lower amplitudes. Monotony and stereotypy, which are often seen in children with movement disorders, are negative aspects of variability and complexity. Data derived from the ETS represented stereotypic movements when single ankle changes were plotted against time. They were characterized as repetitive occurring identically formed curves with similar movement amplitudes. These movements were only detected in infants with definitely abnormal GM findings. Conclusion: The quality of movements which regards variability, complexity and fluency leads to the GM categories of abnormal and normal movements. For the first time, algorithms were found to evaluate the variability besides the complexity of spontaneous movements. Nevertheless, further investigations and analyses are needed to reach the aim of an automatic discrimination of pathological infant movement patterns.
Published Version
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