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 five input patterns identified from polysomnographic recordings on 20 s frames and assigns them to one out of five possible classes (WA, NREM-I, NREM-II, NREM-III and 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. Papers from the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, October 25-28, 2001, held in Istanbul, Turkey. See also ADM001351 for entire conference on cd-rom.