Support vector machine classification of multi-channel EEG traces: a new tool to analyze the brain response to morphine treatment.

The analgesic effect of morphine is highly individual, calling for objective methods to predict the subjective pain relief. Such methods might be based on alteration of brain response caused by morphine during painful stimuli. The study included 11 healthy volunteers subjectively quantifying perception of painful electrical stimulations in the esophagus. Brain evoked potentials following stimulations were recorded from sixty-four electroencephalographic channels at baseline and ninety minutes after morphine administration. Marginals obtained from discrete wavelet coefficients for each channel were used as input to an optimized support vector machine classifying between baseline and after morphine administration. The electroencephalographic channel leading to the best performance was further analyzed to identify brain alterations caused by morphine. Marginals from volunteers with no analgesic effect were examined for differences in comparison to volunteers with effect. The single-channel classification showed best performance at electrode P4 with 84.1 % of the traces classified correctly. When combining features from the 6 best performing channels, the multichannel classification increased to 92.4 %. The most discriminative feature was a decrease in the delta band (0.5 - 4 Hz) after morphine for volunteers with analgesic effect. Volunteers with no effect of morphine showed an increase in the delta band after drug administration. As only a proportion of patients benefit from opioid treatment, the new approach may help to identify non-responders and guide individualized tailored analgesic therapy.