Electroencephalogram fractal dimension as a measure of depth of anesthesia

Abstract

This paper proposes a combined method including adaptive segmentation and Higuchi fractal dimension (HFD) of electroencephalograms (EEG) to monitor depth of anesthesia (DOA). The EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. Due to the nonstationary nature of EEG signal, adaptive segmentation methods seem to have better results. The HFD of a single channel EEG was computed through adaptive windowing methods consist of adaptive variance and auto correlation function (ACF) based methods. We have compared the results of fixed and adaptive windowing in different methods of calculating HFD in order to estimate DOA. Prediction probability (Pk) was used as a measure of correlation between the predictors and BIS index to evaluate our proposed methods. The results show that HFD increases with increasing BIS index. In ICU, all of the methods reveal better performance than in other groups. In both ICU and operating room, the results indicate no obvious superiority in calculating HFD through adaptive segmentation.

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Identifiers

ORCID for Bahareh Zaghari: orcid.org/0000-0002-5600-4671

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