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Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 ± 1.8 days (average ± SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.

Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms