Learning to Classify Biomedical Signals Miroslav Kubat,Irena Koprinska and Gert Pfurtscheller

Contents Two Medical Domain  Decision-Tree Based Initialization of Neural Networks  Tree-Based Initialization of RBF Networks  Experiments  Discussion

Two Medical Domains Sleep Classification Hypnogram : Horizontal axis~Time Vertical axis~ different sleep state To draw a hypnogram: EEG(electroencephalogram) ~ brain activities EOG(electrooculogram) ~ eye movements EMG(electromyogram) ~ muscle contractions

Two Medical Domains Brain-Computer Interface Recognition of Motor Commands From EEG Signals

Problem The complicated nature of domains  impossible to use symbolic machine learning tech. such as rule or decision trees. Multilayer neural network  sensitive to proper initialization of topology and weight.

Decision-Tree Based Initialization of Neural Networks General Idea of TBNN

Decision-Tree Based Initialization of Neural Networks Initialization of Weights and Full interconnection of Adjacent Layers OR-neuron

AND -neuron

Decision-Tree Based Initialization of Neural Networks Softening Intervals and Neural-Network Tuning

Tree-Based Initialization of RBF Networks RBF Networks and their Parameters

Tree-Based Initialization of RBF Networks Decision Tree Based Parameter Setting

Experiment Data set  domain 1 ~ 8 data files 770~990 examples 15 attributes domain 2 ~ 3 data files 150~250 examples 11 attributes ~ 44 attributes

Experiments Results

Result and Discussion First domain(sleep state)  The accuracy achieved by TBNN and TB-RBF is not worse that that of human experts Second domain(brain computer interface) The utility is obvious because the patterns of desynchronization of EEG are difficult of describe by rules, and learning appears to be only way to accomplish the task.