Title: Fractal dimension of electroencephalogram for assessment of hypnosis state of patient during anaesthesia

Authors: Sanjeev Kumar; Amod Kumar; Satinder Gombar; Anjan Trikha; Sneh Anand

Addresses: Biomedical Instrumentation Unit, Central Scientific Instruments Organisation Sector 30-C, Chandigarh-160030, India ' Biomedical Instrumentation Unit, Central Scientific Instruments Organisation Sector 30-C, Chandigarh-160030, India ' Department of Anaesthesia & Intensive Care Government Medical College & Hospital Sector 32, Chandigarh, India ' Department of Anesthesiology, All Indian Institute for Medical Sciences, Ansari Nagar, New Delhi-110029, India ' Centre for Biomedical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India

Abstract: Depth of Anaesthesia (DoA) measurement and control is a demanding task that must be done to avoid intraoperative awareness and explicit recall of pain during surgery. Conventional methods of assessing DoA involve monitoring of physiological parameters, which are not found reliable, as patient awareness during surgery with anaesthetic agents has been reported. Electroencephalogram (EEG) is found to be a reliable means to determine the real anaesthetic state of a patient during surgery. Balanced anaesthesia is the fusion of four different components: hypnosis, analgesia, amnesia and neuromuscular blockade. The accurate control of anaesthesia is possible only with the accurate assessment of the different components of anaesthesia. The major component of balanced anaesthesia is hypnosis, which gives the level of unconsciousness of the patient during surgery. In the present study, efforts were made to calculate and validate an EEG-based parameter which is able to predict the awake and anaesthetic sleep state of the patient. In the present study, the EEG of 60 patients were recorded during normal awake state and while under anaesthesia. Analysis of the EEG signals was performed by non-linear quantifiers. Higuchi's Fractal Dimension (HFD) has been calculated for this recorded EEG waveform for all patients in both states. It was found that HFD is able to predict the awake/sleep state of the patient quite accurately. Validation of the study was done by monitoring BIS in parallel and concluded that the HFD of the EEG goes down as the patient moves into deep hypnosis state.

Keywords: electroencephalograms; EEG signals; balanced anaesthesia; hypnosis state; Higuchi fractal dimension; BIS monitoring; depth of anaesthesia; patient awareness; anaesthesia control; bispectral index; surgery.

DOI: 10.1504/IJBET.2012.049323

International Journal of Biomedical Engineering and Technology, 2012 Vol.10 No.1, pp.30 - 37

Published online: 12 Dec 2014 *

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