A Depth of Anaesthesia Index from Linear Regression of EEG Parameters. Journal of Clinical Monitoring and Computing

Kumar, Amod and Anand, Sneh (2006) A Depth of Anaesthesia Index from Linear Regression of EEG Parameters. Journal of Clinical Monitoring and Computing. Journal of Clinical Monitoring and Computing, 20 (2). pp. 67-73. ISSN 1387-1307

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Objective. The field of Anaesthesia has recently witnessed numerous advances both in the drug administration and monitoring of anaesthetic state. This development has further boosted the efforts and interest of researchers in the automation of clinical Anaesthesia. The success in this direction is possible only when assessment of the depth of hypnotic component of anaesthesia is achieved accurately. This paper describes a technique to arrive at a reliable Depth of Hypnosis (DoH) index using electroencephalographic (EEG) parameters. Methods. EEG data from nine patients was recorded and processed to obtain a total of 21 EEG parameters. They were reduced to a set of best five parameters after applying graphical variance analysis which evaluates their power to discriminate between awake and unresponsive states. These five parameters were normalized with respect to awake state and used in a first order equation to give DoH index. Results. The value of computed DoH index varied from 0.37 to 0.58 for different patients during anesthetized state (awake value 1). For a single patient, the maximum variation in the index was observed as ±5% for different epochs at constant dose. Conclusions. A combination of irregularity of EEG waveform in time-domain and band powers in frequency domain best describes the difference between awake and anesthetized states. To characterize these states, a set of optimum EEG parameters exists. These parameters must be normalized to reduce interpatient variability. The calculated graded index may be used to assist the anaesthetist in the operating theatre.

Item Type: Article
Uncontrolled Keywords: Depth of Hypnosis index; EEG parameters; variance analysis; normalization of parameters; linear regression
Subjects: CSIO > Medical Instrumentation
Depositing User: Ms. J Shrivastav
Date Deposited: 03 Apr 2012 10:07
Last Modified: 03 Apr 2012 10:07
URI: http://csioir.csio.res.in/id/eprint/63

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