Classification of sleep stages in infants: a neuro fuzzy approach

An ANFIS based neuro-fuzzy system to classify sleep-waking states and stages in healthy infants has been developed. The classifier takes rive input patterns identified from polysomnographic recordings on 20 s frames and assigns them to one out of rive possible classes (WA, NREM-I, NREM-II, NREM-III&IV or REM). Eight polysomnographic recordings of healthy infants were studied, making a total of 3510 frames. Of these, four recordings were used for training, two for validation and two for testing. Results on the testing data achieved on average 88.2% of expert agreement in sleep-waking state-stage classification. These results were compared with the ones obtained using a multi-layer perceptron neural network (87.3%) and by applying the expert´s rules for sleep classification (86.7%). The neuro-fuzzy approach also rendered fuzzy classification rules, which were analyzed and compared with the expert´s rules.