Given a record of instruments values over time, a user can mark the record t select the values of particular instruments during particular time ranges (Fig. 4 item 402). They can further indicate the events (states) associated with those values and time ranges (Fig. 4 item 404). These markings define the topology of the PNN. The selected instruments define the input nodes of te PNN and the event(s) detected, define theclass nodes wherein each event has a cossesponding positive class node and a corresponding negative class node. Upon construction the PNN, training may be added to further refine the knowledge of the neural network in a time efficient manner (Fig. 4 item 406). Because the optimal value of sigma varies little over training set sizes, training cases may be incrementally added to the PNN, further adding to its recognition capabilities, without having to train the PNN on the new cases or re-train on the old cases(Fig. 4 item 410). As such, patient specific neural networks may be created in a time and cost efficient manner.
SYSTEMS AND METHODS FOR AUTOMATIC AND INCREMENTAL LEARNING OF PATIENT STATES FROM BIOMEDICAL SIGNALS