Methodology for hypoglycaemia detection based on the processing, analysis and classification of the electroencephalogram.

Hypoglycaemia (blood glucose level below 3.8 mmol l(-1)) is the most common complication in the treatment of diabetes with insulin and can cause a number of problems. Previous works have shown that hypoglycaemia causes changes in the electroencephalogram (EEG) signal. In this investigation, portable apparatus was developed to record the EEG, and a methodology was implemented, using digital signal processing and artificial neural networks (ANNs), to detect hypoglycaemia. Sixteen EEG recordings were made on eight subjects with diabetes (five male, three female), aged 35 +/- 13.5 years (mean +/- SD), during the day, over periods of 5.7 +/- 2 min. Ten of these recordings (in seven subjects) included periods of normoglycaemia and spontaneous hypoglycaemia. The result of the off-line ANN classification for each of these ten recordings was an overall accuracy rate of 71.3%, sensitivity of 71.1% and specificity of 71.5%. In the classification using four recordings from a single subject, the accuracy was 80.6%, with a sensitivity of 77.8% and a specificity of 83.9%. In the classification using recordings from five different subjects to train the ANN, the obtained accuracy rate was 49.2%, with a sensitivity of 76% and a specificity of 32.5%. The result of the classification in real time, for one subject, was an accuracy rate of 85.2%, with a sensitivity of 60% and a specificity of 100%. In conclusion, the methodology proposed and implemented justifies further studies with the objective of constructing a hypoglycaemia detector system based on the processing and classification of the EEG.