Simulation method of skull remodellingsurgeryfor infant with craniosynostosis / 生物医学工程学杂志

Craniofacial malformation caused by premature fusion of cranial suture of infants has a serious impact on their growth. The purpose of skull remodeling surgery for infants with craniosynostosis is to expand the skull and allow the brain to grow properly. There are no standardized treatments for skull remodeling surgery at the present, and the postoperative effect can be hardly assessed reasonably. Children with sagittal craniosynostosis were selected as the research objects. By analyzing the morphological characteristics of the patients, the point cloud registration of the skull distortion region with the ideal skull model was performed, and a plan of skull cutting and remodeling surgery was generated. A finite element model of the infant skull was used to predict the growth trend after remodeling surgery. Finally, an experimental study of surgery simulation was carried out with a child with a typical sagittal craniosynostosis. The evaluation results showed that the repositioning and stitching of bone plates effectively improved the morphology of the abnormal parts of the skull and had a normal growth trend. The child's preoperative cephalic index was 65.31%, and became 71.50% after 9 months' growth simulation. The simulation of the skull remodeling provides a reference for surgical plan design. The skull remodeling approach significantly improves postoperative effect, and it could be extended to the generation of cutting and remodeling plans and postoperative evaluations for treatment on other types of craniosynostosis.

Constrained ICA and its application to removing artifacts in EEG / 生物医学工程学杂志

Independent component analysis (ICA) is a statistic technique which extracts independent components from a set of standard signals. Since Electroencephalogram (EEG) signals are the mixture of several relatively independent sources, ICA has attracted extensive attention in the field of EEG processing. In this paper, a new Constrained ICA (cICA) algorithm is introduced, it would solve the problem of orderless output when FastICA algorithm is used. The experiment results testify that the cICA algorithm can reduce the effect of different individual when the artifacts of EEG are removed manually. The results also show that the cICA algorithm is robust and performs faster convergence.