Assessment of depth of anesthesia using principal component analysis

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

Point Selection for Triangular 2-D Mesh Design Using Adaptive Forward Tracking Algorithm

Two-dimensional mesh-based motion tracking preserves neighboring relations (through connectivity of the mesh) and also allows warping transformations between pairs of frames;thus, it effectively eliminates blocking artifacts that are common in motion compensation by block matching. However, available uniform 2-D mesh model enforces connec-tivity everywhere within a frame, which is clearly not suitable across occlusion boundaries. To overcome this limitation, BTBC (background to be covered) detection and MF (model failure) detection algorithms are being used. In this algorithm, connectivity of the mesh elements (patches) across covered and uncovered region boundaries are broken. This is achieved by allowing no node points within the background to be covered and refining the mesh structure within the model failure region at each frame. We modify the occlusion-adaptive, content-based mesh design and forward tracking algorithm used by Yucel Altunbasak for selection of points for triangular 2-D mesh design. Then, we propose a new triangulation procedure for mesh structure and also a new algorithm to justify connectivity of mesh structure after motion vector estimation of the mesh points. The modified content-based mesh is adaptive which eliminates the necessity of transmission of all node locations at each frame.

Comparison of Correlation Dimension and Fractal Dimension in Estimating BIS index

This paper compares the correlation dimension (D2) and Higuchi fractal dimension (HFD) approaches in estimating BIS index based on of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. For better analysis, application of adaptive segmentation on EEG signal for estimating BIS index is evaluated and compared to fixed segmentation. Prediction probability (PK) is used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. The results show the ability of these algorithms (specifically HFD algorithm) in predicting BIS index. Also, evolving fixed and adaptive windowing methods for segmentation of EEG reveals no meaningful difference in estimating BIS index.