Intelligent signal processing of evoked potentials for anaesthesia monitoring and control

Depth of anaesthesia is hard to define and not readily measurable. Recently, attention has tumid to evoked potentials (EPs) rather than the electroencephalogram (EEG) and they have been validated as a good measure of depth of anaesthesia. However, the amplitudes of the EPs vary from tenths of a microvolt to a few microvolts (mu V). and are embedded in the spontaneous EEG waveform whose amplitude is typically 10 to 30 mu V. Thus. in most instances the signal-to-noise ratio (S/N) is less than 1:10 (-20 dB). It is this small signal-to-noise ratio that makes waveform and signal estimation classification difficult. Therefore. an intelligent signal processing methodology for evoked potentials in anaesthesia monitoring and control is proposed in the paper. A model-based algorithm based upon auto-regressive with exogenous input (ARX) models is used to improve the signal-to-noise ration (S/N). Quantitative feature extraction is implemented to extract the factors describing the changes in amplitudes and latencies of the mid-latency auditory evoked response. In this way, three principal factors are obtained and then merged together using qualitative fuzzy logic to create a reliable index for monitoring depth of anaesthesia. Twenty-one clinical trials were carried out to validate this methodology as a reliable assessment of anaesthetic depth during intravenous anaesthesia using propofol.