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V. Lalitha and C. Eswaran, “Automated detection of anesthetic depth levels using chaotic features with artificial neural networks,” Journal of Medical Systems, No. 31, pp. 445–452, 2007.

has been cited by the following article:

• TITLE: Comparison of Correlation Dimension and Fractal Dimension in Estimating BIS index

  AUTHORS: Behzad AHMADI, Rassoul AMIRFATTAHI

  KEYWORDS: Adaptive Segmentation, Bispectral Index, Depth of Anesthesia, Correlation Dimension, Fractal Dimension

  JOURNAL NAME: Wireless Sensor Network, Vol.2 No.1, January 13, 2010

  ABSTRACT: 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.